Digital asset staking method using blockchain electronic wallet based on multi-party computation

Abstract

A withdrawal method using a side chain of the invention in the present application may comprise the steps of: generating at least one multi-party computation wallet; allocating a key share to each of the at least one multi-party computation wallet; and receiving a request for asset custody from a user of the at least one multi-party computation wallet and depositing assets of the at least one multi-party computation wallet in a staking wallet.

Description

A method for staking digital assets using a multi-party computing-based blockchain electronic wallet.

[0001] The present invention relates to an anomaly detection system that determines whether a digital asset transaction is an anomaly. Furthermore, the present invention relates to a staking service system that provides deposit services for virtual assets traded through a verifier capable of depositing only virtual assets of a pre-fixed value. Furthermore, the present invention relates to a withdrawal method using a sidechain based on a custody system.

[0002] Recently, the use of blockchain technology has enabled the implementation of a distributed ledger that eliminates intermediaries, enabling trust between participants and preventing falsification. This has led to the emergence of digital assets such as cryptocurrencies. A new asset management service targeting these digital assets, called digital asset custody, has emerged. Digital asset custody is a blockchain-based asset storage and management service for digital assets. It represents an infrastructure business for the digital asset market, similar to the custody business of banks.

[0003] To stabilize and foster healthy development in the digital asset market, various institutional and technological complementary measures are required. Custody services are expected to serve as industrial infrastructure and a gateway to this process. In particular, the activation of custody services is expected to facilitate the institutionalization of digital assets, enhance and strengthen trust in the digital asset market, remove technical barriers to entry for general investors, and strengthen internal controls and transparent disclosure of digital asset transactions.

[0004] The purpose of the present invention is to provide a staking service provision system that can provide a deposit service for virtual assets traded through a verifier that can only deposit virtual assets of a pre-fixed value.

[0005] In addition, the present invention aims to provide an abnormal transaction detection system that can increase the detection rate for abnormal transactions through multiple artificial intelligence models.

[0006] In addition, the present invention aims to provide an abnormal transaction detection system that can transmit a suspicious transaction notification to an internal transaction analysis team when a transaction of digital assets is determined to be an abnormal transaction.

[0007] In addition, the present invention aims to provide an abnormal transaction detection system that can take measures in accordance with internal regulations and related systems for transactions finally classified as abnormal transactions based on additional detailed analysis by an internal transaction analysis team or a final judgment on whether or not an abnormal transaction is made by members of an internal monitoring and control organization.

[0008] In addition, the present invention aims to provide an abnormal transaction detection system that can improve performance, such as increasing data processing speed or reducing required memory capacity, compared to conventional systems, by not having to separately execute multiple programs when detecting abnormal transactions.

[0009] In addition, the present invention aims to provide a staking service provision system that can solve the problem of difficulty in using services for virtual assets traded through a validator that can only deposit virtual assets of a fixed value in advance, unless all relatively expensive virtual assets of a fixed value are deposited.

[0010] In addition, the present invention relates to a withdrawal method using key shares in a staking service.

[0011] The technical problems of the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art of the present invention from the description below.

[0012] According to an embodiment of the present invention, digital assets can be entrusted through a custody system and effectively stored and managed, and digital assets of customers entrusted to the custody system can be managed based on machine learning.

[0013] Furthermore, according to an embodiment of the present invention, by utilizing various artificial intelligence models to accurately predict the price increase rate and accuracy for each period for each exchange, a digital asset portfolio suitable for the customer's investment tendency can be constructed and the customer's asset investment can be performed on his / her behalf.

[0014] According to one embodiment of the present invention, a custody system using a withdrawal method using a side chain can be provided.

[0015] Figure 1 is a configuration diagram of a token withdrawal transaction fee payment system utilizing a smart contract according to an embodiment of the present invention.

[0016] Figure 2 is a flowchart of a method for paying token withdrawal transaction fees using a smart contract according to an embodiment of the present invention.

[0017] Figure 3 is a conceptual diagram for explaining step S100 of Figure 2.

[0018] Figure 4 is a conceptual diagram for explaining step S200 of Figure 2.

[0019] Figure 5 is a conceptual diagram for explaining step S300 of Figure 2.

[0020] Figure 6 is a conceptual diagram for explaining step S400 of Figure 2.

[0021] Figure 7 is a configuration diagram of an abnormal transaction detection system according to an embodiment of the present invention.

[0022] Figure 8 is a table showing deposit and withdrawal data of customers using a virtual asset custody service according to an embodiment of the present invention.

[0023] FIG. 9 is a diagram for explaining a machine learning-based abnormal transaction detection method according to an embodiment of the present invention.

[0024] Figure 10 is a flowchart of a control method of an abnormal transaction detection system according to an embodiment of the present invention.

[0025] Figure 11 is a flowchart of a method of taking action against a consignor terminal determined to be an abnormal transaction according to an embodiment of the present invention.

[0026] FIG. 12 is a diagram for explaining asset staking through a custody system in a staking service providing system according to an embodiment of the present invention.

[0027] FIG. 13 is a diagram illustrating a system for providing a staking service through a custody system according to an embodiment of the present invention.

[0028] FIG. 14 is a diagram for explaining generating verifier deposit data and registering a verifier according to an embodiment of the present invention.

[0029] FIG. 15 is a diagram for explaining staking with a proxy account according to an embodiment of the present invention.

[0030] FIG. 16 is a diagram for explaining transferring assets to a customer by unstaking according to an embodiment of the present invention.

[0031] Fig. 17 is a flowchart of a control method of a staking service providing system according to an embodiment of the present invention.

[0032] Figure 18 is a flowchart of a withdrawal method using a side chain based on a custody system according to one embodiment.

[0033] Figure 19 is a diagram explaining a withdrawal method using a side chain based on a custody system.

[0034] An abnormal transaction detection system for determining whether a transaction of a digital asset occurring in a custody system configured to provide a service for managing digital assets according to one embodiment is an abnormal transaction may include an analyzer terminal configured to determine whether a transaction to be inspected is an abnormal transaction, wherein the analyzer terminal may include: a deposit / withdrawal data receiving unit configured to receive deposit / withdrawal data of the transaction to be inspected from the custody system; and an abnormal transaction analysis unit configured to determine whether the transaction to be inspected is an abnormal transaction subject to a transaction suspension using an artificial intelligence model based on the deposit / withdrawal data.

[0035] In addition, the above-mentioned analyst terminal may further include a machine learning unit configured to generate the artificial intelligence model through a machine learning method by using the deposit / withdrawal data of normal transactions for learning and the deposit / withdrawal data of abnormal transactions for learning as input variables, and setting the normal or not information corresponding to the normal transactions for learning and the normal or not information corresponding to the abnormal transactions for learning as output variables.

[0036] In addition, the machine learning unit may be configured to generate the artificial intelligence model through a machine learning method by setting as input variables an abnormal transaction for learning corresponding to at least one of a transaction of digital assets executed by a consignor who has had a reason for a transaction suspension in the past, a transaction of digital assets of a person subject to a withdrawal who has had a reason for a transaction suspension in the past, an abnormal self-trading, an abnormal high-price transaction, and an abnormal low-price transaction, and setting as output variables information on whether the abnormal transaction for learning is normal.

[0037] In addition, the method further includes an action terminal configured to determine whether to suspend a transaction for a consignor terminal related to the transaction to be inspected, and the analyzer terminal may be configured to transmit a warning signal to the action terminal if the transaction to be inspected is determined to be an abnormal transaction.

[0038] Additionally, the action terminal may be configured to transmit a transaction stop signal to the custody system to stop the transaction subject to inspection when receiving the warning signal.

[0039] In addition, the action terminal further includes an input unit configured to receive input information from an administrator, and after transmitting the transaction suspension signal, when the input unit receives normal confirmation information indicating that the transaction subject to inspection is a normal transaction, the action terminal may be configured to transmit a transaction resumption signal to the custody system indicating that the suspension of the transaction subject to inspection is canceled.

[0040] In addition, the above-mentioned action terminal may be configured to transmit a signal to the custody system to take action in accordance with the law with respect to the transaction subject to inspection when, after transmitting the transaction stop signal, the input unit receives abnormality confirmation information indicating that the transaction subject to inspection is confirmed to be an abnormal transaction.

[0041] In addition, the machine learning unit may be configured to generate a plurality of different artificial intelligence models based on different, separate machine learning algorithms, and the abnormal transaction analysis unit may be configured to: obtain a result of determining whether the transaction to be inspected is an abnormal transaction, which is output by each of the plurality of artificial intelligence models, based on the deposit / withdrawal data; and determine whether the transaction to be inspected is an abnormal transaction based on the number of artificial intelligence models that determined the transaction to be inspected to be an abnormal transaction.

[0042] In addition, the above-mentioned abnormal transaction analysis unit may be configured to: determine a weight corresponding to each artificial intelligence model based on the detection rate for abnormal transactions for learning of each of the artificial intelligence models; and determine whether the transaction to be inspected is an abnormal transaction based on the weight of the artificial intelligence model that determined the transaction to be inspected to be an abnormal transaction and the weight of the artificial intelligence model that determined the transaction to be inspected to be a normal transaction.

[0043] In addition, the machine learning unit is configured to: generate a first artificial intelligence model through a machine learning method of a machine learning algorithm based on a distance-based outlier detection model (K-Nearest Neighbors; KNN); generate a second artificial intelligence model through a machine learning method of a machine learning algorithm based on a cluster-based outlier detection model (K-Means Clustering); generate a third artificial intelligence model through a machine learning method of a machine learning algorithm based on a principal component analysis (PCA) model; generate a fourth artificial intelligence model through a machine learning method of a machine learning algorithm based on a support vector machine (SVM); and generate a fifth artificial intelligence model through a machine learning method of a machine learning algorithm based on an auto encoder; and the abnormal transaction analysis unit: obtains, based on the deposit / withdrawal data, results of determining whether the transaction to be inspected is an abnormal transaction, which are output by each of the first artificial intelligence model, the second artificial intelligence model, the third artificial intelligence model, the fourth artificial intelligence model, and the fifth artificial intelligence model; And, it can be configured to determine whether the transaction to be inspected is an abnormal transaction based on the weight of the artificial intelligence model that determines the transaction to be inspected as an abnormal transaction and the weight of the artificial intelligence model that determines the transaction to be inspected as a normal transaction.

[0044]

[0045] A staking service providing system according to one embodiment may include: a custody system configured to provide a service for managing a virtual asset to be traded through a validator, and to deposit a virtual asset to be traded at a pre-fixed value per validator; a smart contract configured to receive a transaction fee on behalf of the custody system through a payment account, and to perform a transaction of a virtual asset to be traded corresponding to a consignor terminal based on a signal received from the consignor terminal; and a staking contract configured to allow the smart contract to deposit a virtual asset to be traded at a pre-fixed value per validator based on a signal received from the consignor terminal.

[0046] In addition, the custody system may be configured to: upon receiving a staking request signal from the consignor terminal, generate validator deposit data through a node of the virtual asset to be traded; register the validator with the node of the virtual asset to be traded based on the validator deposit data; and store the validator deposit data in a custody database.

[0047] In addition, the verifier deposit data includes a keystore file and deposit data, and the custody system may be configured to: upon receiving a staking request signal from the consignor terminal, generate the keystore file and the deposit data; transmit the keystore file to the node of the virtual asset to be traded; and transmit the deposit data to the consignor terminal.

[0048] In addition, the custody system may be configured to execute staking of the asset deposited in the smart contract together with the verifier deposit data by transferring the virtual asset to be traded, deposited in the smart contract by the entrustor terminal, to the smart contract through the payment account when receiving a staking request signal from the entrustor terminal, together with the verifier deposit data.

[0049] In addition, the custody system may be configured to, when receiving a staking request signal from one of the consignor terminals, determine whether the value of the virtual asset to be traded, for which the consignor terminal requested a deposit, corresponds to a value fixed in advance for each of the verifiers.

[0050] In addition, the custody system may be configured to generate verifier deposit data corresponding to the one entrustor terminal and register the verifier corresponding to the one entrustor terminal in the node of the transaction target virtual asset when the value of the transaction target virtual asset for which a deposit has been requested by one entrustor terminal corresponds to a value fixed in advance for each verifier.

[0051] In addition, the custody system may be configured to: combine the trustor terminals so that the sum of the values ​​of the virtual assets to be traded requested by the plurality of trustor terminals becomes a pre-fixed value per verifier by combining the values ​​of the virtual assets to be traded requested by at least one other trustor terminal with the values ​​of the virtual assets to be traded requested by the trustor terminals, if the value of the virtual assets to be traded requested by one trustor terminal is less than a pre-fixed value per verifier; and generate a unique number corresponding to the combined plurality of trustor terminals so that the sum of the values ​​of the virtual assets to be traded requested becomes a pre-fixed value per verifier.

[0052] In addition, the custody system may be configured to generate, when a unique number corresponding to the combined plurality of consignor terminals is generated, a verifier deposit data corresponding to the single unique number and register the verifier corresponding to the single unique number in the node of the virtual asset to be traded.

[0053] In addition, the staking contract may be configured to deposit the principal and interest of the verifier corresponding to the entrustor terminal into the payment account when the smart contract receives a withdrawal request signal and the deposit data from the entrustor terminal.

[0054] In addition, the staking contract is configured to deposit the principal and interest of the verifier corresponding to the unique number into the payment account when the smart contract receives a withdrawal request signal and the deposit data from any one of the payment account holder terminals corresponding to the unique number after a unique number corresponding to the combined plurality of payment account holder terminals is generated, and the smart contract may be configured to distribute the principal and interest deposited into the payment account to the payment account holder terminals corresponding to the unique number.

[0055]

[0056] A withdrawal method using a side chain based on a custody system according to one embodiment is a withdrawal method using a side chain based on a custody system performed by at least one processor, the method comprising: creating at least one multi-party computation wallet; allocating a key share to each of the at least one multi-party computation wallet; receiving a request for asset custody from a user of the at least one multi-party computation wallet, and depositing the asset of the at least one multi-party computation wallet into a staking wallet; requesting a signature from a user of the at least one multi-party computation wallet regarding an asset transfer of the staking wallet; receiving signature data from a user of the at least one multi-party computation wallet; and performing an asset transfer of the staking wallet when the received signature data is equal to or greater than a signature threshold.

[0057] Here, the step of depositing the above assets into a staking wallet can be performed when the assets collected from at least one multi-party computation wallet are greater than the minimum staking amount.

[0058] Here, a step of paying interest to at least one multi-party computation wallet may be further included based on the size of assets deposited in the staking wallet.

[0059] Here, the signature threshold can be set by the consent of users of at least one multi-party operation wallet.

[0060] Here, the step of allocating the key share may be a step of allocating one key share per multi-party operation wallet.

[0061] Here, the above signature data can be generated using a key share assigned to a multi-party computation wallet.

[0062] Here, a computer program stored in a computer-readable recording medium may be provided to execute a withdrawal method using a side chain based on the custody system.

[0063] The advantages and features of the present invention, and the methods for achieving them, will become clearer with reference to the embodiments described in detail below together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below and may be implemented in various different forms. These embodiments are provided only to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the invention of the scope of the invention, and the present invention is defined only by the scope of the claims. Like reference numerals designate like elements throughout the specification.

[0064] In this specification, when a part is said to 'include' a certain component, this does not mean that other components are excluded, but rather that other components can be included, unless otherwise specifically stated. The '~ unit' used in this specification refers to a unit that processes at least one function or operation, and may refer to, for example, software, an FPGA, or a hardware component. The function provided by the '~ unit' may be performed separately by multiple components, or may be integrated with other additional components. The '~ unit' in this specification is not necessarily limited to software or hardware, and may be configured to be located in an addressable storage medium, or may be configured to reproduce one or more processors. Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[0065] Since the embodiments described in this specification are intended to clearly explain the idea of ​​the present invention to a person having ordinary skill in the art to which the present invention pertains, the present invention is not limited to the embodiments described in this specification, and the scope of the present invention should be interpreted to include modified or altered examples that do not depart from the idea of ​​the present invention.

[0066] The terms used in this specification have been selected from widely used terms, taking into account their functions in the present invention. However, these terms may vary depending on the intentions of those skilled in the art, precedents, or the emergence of new technologies in the technical field to which the present invention pertains. However, if a specific term is defined and used with an arbitrary meaning, the meaning of that term will be described separately. Therefore, the terms used in this specification should be interpreted based on the actual meaning of the term and the overall content of this specification, rather than simply the name of the term.

[0067] The drawings attached to this specification are intended to facilitate explanation of the present invention, and the shapes depicted in the drawings may be exaggerated as necessary to help understand the present invention, and therefore the present invention is not limited by the drawings.

[0068] In cases where it is determined that a specific description of the composition or function of a public notice related to the present invention in this specification may obscure the gist of the present invention, a detailed description thereof will be omitted as necessary.

[0069]

[0070] A system and method for paying token withdrawal transaction fees using a smart contract according to an embodiment of the present invention can provide a service in which, when a consignor terminal requests a token withdrawal transaction, a custody system withdraws a transaction fee for a token withdrawal transaction through a payment account and pays it to a smart contract.

[0071] A system and method for paying a token withdrawal transaction fee using a smart contract according to an embodiment of the present invention comprises: a smart contract associated with a custody system corresponding to a trustee terminal receives authorization to permit withdrawal of digital assets from a trustor terminal and approves withdrawal of digital assets; when the custody system receives a request for withdrawal of digital assets from a trustor terminal, the custody system verifies data related to withdrawal of digital assets by the verification logic of the custody system; when the verification of data related to withdrawal of digital assets is completed, the custody system withdraws a transaction fee through a payment account of the custody system and pays the transaction fee to the smart contract on behalf of the trustor terminal; and the smart contract receives payment of the transaction fee through the payment account and approves the transfer of tokens related to the digital assets for which withdrawal has been requested by the trustor terminal, thereby transferring the digital assets to the recipient account.

[0072]

[0073] Figure 1 is a diagram illustrating a token withdrawal transaction fee payment system utilizing a smart contract according to an embodiment of the present invention. Referring to Figure 1, a digital asset custody system (10) according to an embodiment of the present invention may include one or more trustor terminals (100), one or more trustee terminals (200), and a trustee database (300).

[0074] The consignor terminal (100) may be a terminal associated with the consignor who entrusts the custody and management of digital assets to the consignee terminal (200). The consignor terminal (100) may be, for example, a terminal belonging to an entity (consignor) such as a company, institution, or organization that holds digital assets.

[0075] The consignor terminal (100) may be provided as a terminal such as a desktop PC, laptop, notebook, smartphone, or smart pad, but is not limited thereto. The consignor terminal (100) may include one or more administrator terminals (110), one or more applicant terminals (120), and one or more approver terminals (130).

[0076] An administrator terminal (110), an applicant terminal (120), an approver terminal (130), and a trustee terminal (200) are connected via a wired / wireless communication network and can communicate with each other. The administrator terminal (110) may be a terminal that requests an owner account, an applicant account, and an approver account from the trustee terminal (200) to entrust the custody and management of digital assets held by a trustor, such as a company, to the trustee.

[0077] The applicant terminal (120) may be a terminal used by an applicant who applies for digital asset management, such as storage, transfer, and withdrawal of digital assets, by accessing an applicant account issued by a trustee terminal (200) in response to a request for digital asset storage and management from an administrator terminal (110).

[0078] In an embodiment of the present invention, the digital asset entrusted by the entrustor to the trustee may include one or more virtual currencies selected from among virtual currencies including Bitcoin, Ethereum, and Klaytn.

[0079] The approver terminal (130) may be a terminal used by an approver to access an approver account issued by a trustee terminal (200) and approve a digital asset management application submitted by an applicant terminal (120).

[0080] The trustee terminal (200) can provide a custody service that entrusts the storage and management of digital assets from the trustor terminal (100) and stores and manages digital assets requested from the trustor terminal (100).

[0081] The trustee database (DB) (300) can store digital asset information entrusted from the trustor terminal (100), owner account information, applicant account information, approver account information, digital asset wallet information (wallet address corresponding to a random number) that stores the entrusted digital asset, etc.

[0082]

[0083] FIG. 2 is a flowchart illustrating a method for paying token withdrawal transaction fees using a smart contract according to an embodiment of the present invention. FIG. 3 is a conceptual diagram illustrating step S100 of FIG. 2 . Referring to FIGS. 2 and 3 , a smart contract (400) associated with a custody system corresponding to a trustee terminal may first be delegated the authority to permit withdrawal of digital assets from one or more trustor terminals (100) and then receive approval for withdrawal of tokens (ERC-20, ERC-721, ERC-1155, etc.) related to digital assets (e.g., virtual currencies such as Bitcoin, Ethereum, and Klaytn) (S100). At this time, the trustor may directly pay the transaction fee to the smart contract (400) through the trustor terminal (100).

[0084] FIG. 4 is a conceptual diagram for explaining step S200 of FIG. 2. Referring to FIG. 2 and FIG. 4, when a custody system (10) receives a request for withdrawal of digital assets from a consignor terminal (100), the custody system (10) can verify data related to withdrawal of digital assets by the verification logic (262) of the custody system (10) (S200).

[0085] FIG. 5 is a conceptual diagram illustrating step S300 of FIG. 2. Referring to FIG. 2 and FIG. 5, when data related to withdrawal of digital assets is verified, the custody system (10) can withdraw a transaction fee, which is a fee related to the transaction of digital assets, through the payment account (12) of the custody system (10) and pay the transaction fee to the smart contract (400) instead of the consignor terminal (S300).

[0086] FIG. 6 is a conceptual diagram illustrating step S400 of FIG. 2. Referring to FIG. 2 and FIG. 6, a smart contract (400) may receive a transaction fee from a custody system (10) through a payment account (12), approve the transfer of tokens related to a digital asset for which a withdrawal request has been made by a consignor terminal (100), and transfer the digital asset to a recipient account related to a recipient terminal (500) (S400).

[0087] According to an embodiment of the present invention, customers, acting as consignor terminals, can more conveniently transfer digital assets to recipients without having to consider transaction fees that fluctuate depending on the type of virtual asset and the transaction situation. Furthermore, custodians can attract customers by prepaying transaction fees, and can also receive a certain amount of commission from customers or generate additional revenue through advertising as customer growth increases.

[0088]

[0089] FIG. 7 is a configuration diagram of an abnormal transaction detection system according to an embodiment of the present invention, and FIG. 8 is a table showing deposit and withdrawal data of customers using a virtual asset custody service according to an embodiment of the present invention.

[0090] Referring to FIG. 7, the abnormal transaction detection system (4000) may include an analyzer terminal (4100) and an action taker terminal (4200).

[0091] The abnormal transaction detection system (4000) may be a system that determines whether a digital asset transaction occurring in a custody system (10) configured to provide a service for managing digital assets is an abnormal transaction. The abnormal transaction detection system (4000) may be installed on the server of a digital asset custody service provider, but is not necessarily limited to being installed on the server of a service provider.

[0092] The analyzer terminal (4100) may be configured to determine whether a transaction under inspection is abnormal. The analyzer terminal (4100) may include a deposit / withdrawal data receiving unit (4110), an abnormal transaction analysis unit (4120), a machine learning unit, and a machine learning unit (4140).

[0093] The deposit / withdrawal data receiving unit (4110) may be configured to receive deposit / withdrawal data for a transaction subject to inspection from the custody system (10). The transaction subject to inspection may be a transaction generated by the custody system (10) for which an abnormality determination is desired. The deposit / withdrawal data receiving unit (4110) may transmit the deposit / withdrawal data for the transaction subject to inspection to the abnormality analysis unit (4120).

[0094] Referring to Figure 8, information included in customer deposit and withdrawal data generated in accordance with the operation of the virtual asset custody service can be confirmed.

[0095] For example, deposit / withdrawal data may include information such as the date and time of the transaction through the custody system (10), the ID and address of the customer who transacted, the country and region where the transaction occurred, and the balance after the transaction. However, the information included in the deposit / withdrawal data is not limited to the information shown.

[0096] The deposit / withdrawal data receiving unit (4110) can receive information included in the deposit / withdrawal data of the transaction to be inspected from the custody system (10) and transmit it to the abnormal transaction analysis unit (4120).

[0097] Referring to FIGS. 7 and 8 , the abnormal transaction analysis unit (4120) can use an artificial intelligence model based on deposit / withdrawal data to determine whether the transaction under inspection is an abnormal transaction subject to transaction suspension. Specifically, the abnormal transaction analysis unit (4120) inputs information contained in the deposit / withdrawal data of the transaction under inspection into the artificial intelligence model, and determines whether the transaction under inspection is an abnormal transaction based on the information output by the artificial intelligence model.

[0098] The machine learning unit (4130) can create an artificial intelligence model through a machine learning method by using the deposit / withdrawal data of normal transactions for learning and the deposit / withdrawal data of abnormal transactions for learning as input variables, and setting the normal or not information corresponding to normal transactions for learning and the normal or not information corresponding to abnormal transactions for learning as output variables.

[0099] Information contained in the deposit / withdrawal data of a normal learning transaction may include information about the transaction amount, customer information, and country or region. The normality information corresponding to a normal learning transaction may include information indicating that the transaction was normal.

[0100] Information contained in the deposit / withdrawal data of a learning-related abnormal transaction may include information about the transaction amount, customer information, and country or region. The normalization information corresponding to a learning-related abnormal transaction may include information indicating that the learning-related abnormal transaction was abnormal or subject to regulation.

[0101] The machine learning unit (4130) can create an artificial intelligence model through a machine learning method by setting as input variables abnormal transactions for learning, which correspond to at least one of digital asset transactions executed by a consignor who had a reason for transaction suspension in the past, digital asset transactions of a withdrawal target who had a reason for transaction suspension in the past, abnormal self-trading, abnormal high-price transactions, and abnormal low-price transactions, and setting as output variables information on whether the abnormal transactions for learning are normal.

[0102] Machine learning utilizes models composed of multiple parameters and can mean optimizing those parameters based on given data. Depending on the type of learning problem, machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Supervised learning learns mappings between inputs and outputs and is applicable when input-output pairs are given as data. Unsupervised learning is applicable when there are only inputs and no outputs, and can identify patterns between inputs, etc.

[0103] The machine learning unit (4130) can create an artificial intelligence model in various ways. For example, the machine learning unit (4130) can learn features extracted from information included in learning-use abnormal transactions or information included in learning-use normal transactions using a deep learning-based learning method. At this time, a CNN (Convolutional Neural Networks) structure that stacks multiple stages of convolution layers can be utilized to learn a method of extracting features from information included in learning-use abnormal transactions or information included in learning-use normal transactions. However, the learning method of the machine learning unit (4130) is not necessarily limited to a method utilizing the CNN structure. For example, the learning method of the machine learning unit (4130) can be a method through a machine learning algorithm including an artificial neural network (ANN), a recurrent neural network (RNN), K-Nearest Neighbors (KNN), K-Means Clustering, Principal Component Analysis (PCA), a Support Vector Machine (SVM), or an Auto Encoder.

[0104] The action agent terminal (4200) may be a terminal of an administrator who manages transactions occurring in the custody system (10) or an action agent who takes action. The action agent terminal (4200) may be configured to determine whether to suspend transactions for a consignor terminal (100) related to a transaction subject to inspection.

[0105] Referring to FIG. 7, if the analysis terminal (4100) determines that the transaction to be inspected is an abnormal transaction, it can transmit a warning signal to the action terminal (4200).

[0106] When the action agent terminal (4200) receives a warning signal, it can output information such as the occurrence of an abnormal transaction and which transaction is the abnormal transaction on an output unit, such as a display, of the action agent terminal (4200). Based on this information, the administrator or action agent using the action agent terminal (4200) can take action on a transaction determined to be an abnormal transaction. For example, the administrator or action agent can precisely analyze whether an abnormal transaction detected through the action agent terminal (4200) is normal. In addition, if there are multiple administrators or action agents, the decision-making of the internal monitoring and control organization can be performed through the action agent terminals (4200) used by each administrator or action agent.

[0107] Upon receiving a warning signal, the action agent terminal (4200) may transmit a transaction stop signal to the custody system (10) to halt the transaction under investigation. Upon receiving the transaction stop signal, the custody system (10) may halt the transaction under investigation corresponding to the transaction stop signal. Furthermore, upon receiving the transaction stop signal, the custody system (10) may also halt or sanction other transactions by the consignor terminal (100) involved in the transaction under investigation.

[0108] The action user terminal (4200) may include an input unit (4210). The input unit (4210) may receive input information from an administrator. The input unit (4210) may receive input information by, but is not limited to, receiving input information by physically pressing a button provided on the action user terminal (4200) from the administrator or the action user, or by using a touch input method on the display.

[0109] Meanwhile, a transaction subject to inspection that was determined to be an abnormal transaction by the analyst terminal (4100) may be confirmed as a normal transaction through a detailed analysis by an administrator or a controller. Furthermore, a transaction subject to inspection that was determined to be an abnormal transaction by the analyst terminal (4100) may be confirmed as a normal transaction through a decision by the internal monitoring and control organization via the controller terminals (4200). In this case, the input unit (4210) of the controller terminal (4200) may receive circumstantial confirmation information from the administrator or controller indicating that the transaction subject to inspection has been confirmed to be a normal transaction.

[0110] After transmitting a transaction stop signal, if the input unit (4210) receives normal confirmation information, the action terminal (4200) can transmit a transaction resumption signal to the custody system (10) to cancel the stop of the transaction to be inspected.

[0111] Upon receiving a transaction resumption signal, the custody system (10) can resume the transaction under investigation that was suspended by the transaction suspension signal. Furthermore, upon receiving a transaction resumption signal, the custody system (10) can also resume other transactions of the consignor terminal (100) involved in the transaction under investigation.

[0112] Meanwhile, a transaction subject to inspection that was determined to be an abnormal transaction by the analyst terminal (4100) may also be confirmed as an abnormal transaction through a detailed analysis by an administrator or a person in charge. Furthermore, a transaction subject to inspection that was determined to be an abnormal transaction by the analyst terminal (4100) may also be confirmed as a normal transaction through a decision by the internal monitoring and control organization via the person in charge terminal (4200). In this case, the input unit (4210) of the person in charge terminal (4200) may receive circumstantial confirmation information from the administrator or person in charge indicating that the transaction subject to inspection has been confirmed to be an abnormal transaction.

[0113] After transmitting a transaction stop signal, if the input unit (4210) receives abnormality confirmation information indicating that the transaction subject to inspection is an abnormal transaction, the action terminal (4200) can transmit a signal to the custody system (10) to the effect that action in accordance with the law is to be taken with respect to the transaction subject to inspection.

[0114] When the custody system (10) receives a signal indicating that it is to take legal action against a transaction subject to inspection, it can proceed with a preset process in accordance with the law for the transaction subject to inspection that was suspended by the transaction suspension signal. Furthermore, when the custody system (10) receives a signal indicating that it is to take legal action against a transaction subject to inspection, it can also proceed with a preset process in accordance with the law for other transactions of the consignor terminal (100) involved in the transaction subject to inspection.

[0115] FIG. 9 is a diagram for explaining a machine learning-based abnormal transaction detection method according to an embodiment of the present invention.

[0116] Referring to FIG. 9, the abnormal transaction detection system (4000) inputs virtual asset deposit / withdrawal data of the transaction to be inspected into multiple artificial intelligence models, and can classify the transaction to be inspected as an abnormal transaction based on information output by the multiple artificial intelligence models.

[0117] Referring to FIGS. 7 and 9, the machine learning unit (4130) can generate multiple different artificial intelligence models based on different, separate machine learning algorithms.

[0118] The abnormal transaction analysis unit (4120) can obtain the results of determining whether a transaction to be inspected, output by multiple artificial intelligence models, is an abnormal transaction based on deposit / withdrawal data.

[0119] The abnormal transaction analysis unit (4120) can determine whether a transaction subject to inspection is an abnormal transaction based on the number of artificial intelligence models that have determined the transaction subject to inspection to be an abnormal transaction.

[0120] Specifically, the abnormal transaction analysis unit (4120) can classify the transaction under inspection as an abnormal transaction if the number of AI models that determine the transaction under inspection as an abnormal transaction is equal to or greater than a preset number. For example, if the preset number is 3 and the number of AI models that classify the transaction under inspection as an abnormal transaction is 4 among 5 AI models, the abnormal transaction analysis unit (4120) can classify the transaction under inspection as an abnormal transaction.

[0121] The abnormal transaction analysis unit (4120) can determine weights for each AI model based on the detection rate of abnormal transactions for learning purposes for each AI model. For example, if there are five AI models, weights corresponding to each AI model can be determined in the following order: 5, 4, 3, 2, and 1, in descending order of detection rate for abnormal transactions for learning purposes.

[0122] The abnormal transaction analysis unit (4120) can determine whether the transaction to be inspected is an abnormal transaction based on the weight of the artificial intelligence model that determined the transaction to be inspected as an abnormal transaction and the weight of the artificial intelligence model that determined the transaction to be inspected as a normal transaction.

[0123] Specifically, the abnormal transaction analysis unit (4120) can classify the transaction under inspection as an abnormal transaction if the sum of the weights of the artificial intelligence models that determined the transaction under inspection as an abnormal transaction is greater than or equal to a preset value. For example, if the preset value is 11 and the weights of the artificial intelligence models that classified the transaction under inspection as an abnormal transaction are 5, 4, and 3, the sum of these is 12, which is greater than or equal to the preset value, and therefore the abnormal transaction analysis unit (4120) can classify the transaction under inspection as an abnormal transaction.

[0124] The machine learning unit (4130) can generate the first artificial intelligence model (4141) through a machine learning method of a machine learning algorithm based on a distance-based outlier detection model (K-Nearest Neighbors; KNN). The machine learning algorithm based on the K-Nearest Neighbors model may be an algorithm that selects several points closest to an input feature vector and uses their labels.

[0125] The machine learning unit (4130) can generate a second artificial intelligence model (4142) through a machine learning method of a machine learning algorithm based on a cluster-based outlier detection model (K-Means Clustering). The machine learning algorithm based on the cluster-based outlier detection model is a learning method for clustering given inputs, and may be an algorithm that assumes that there are a total of K clusters and finds K centroids in the feature space.

[0126] The machine learning unit (4130) can generate a third artificial intelligence model (4143) through a machine learning method of a machine learning algorithm based on a principal component analysis (PCA) model. The machine learning algorithm based on a principal component analysis model may be an algorithm that utilizes a technique for transforming high-dimensional space data into low-dimensional space while preserving the distribution of the original data as much as possible.

[0127] The machine learning unit (4130) can generate the fourth artificial intelligence model (4144) through a machine learning method of a machine learning algorithm based on a support vector machine (SVM). The machine learning algorithm based on a support vector machine may be an algorithm that utilizes an optimal hyperplane that can distinguish between two given categories of data in a feature space.

[0128] The machine learning unit (4130) can generate the fifth artificial intelligence model (4145) through a machine learning method of an auto-encoder-based machine learning algorithm. The auto-encoder-based machine learning algorithm is an unsupervised learning algorithm that learns a function that approximates the input value with an output value.

[0129] The abnormal transaction analysis unit (4120) can obtain the results of determining whether the transaction to be inspected, which is output by the first artificial intelligence model (4141), the second artificial intelligence model (4142), the third artificial intelligence model (4143), the fourth artificial intelligence model (4144), and the fifth artificial intelligence model (4145), is an abnormal transaction based on deposit / withdrawal data.

[0130] The abnormal transaction analysis unit (4120) can determine whether the transaction to be inspected is an abnormal transaction based on the weight of the artificial intelligence model that determined the transaction to be inspected as an abnormal transaction and the weight of the artificial intelligence model that determined the transaction to be inspected as a normal transaction.

[0131] Figure 10 is a flowchart of a control method of an abnormal transaction detection system according to an embodiment of the present invention.

[0132] Referring to FIG. 10, the deposit / withdrawal data receiving unit (4110) can receive deposit / withdrawal data of the transaction to be inspected from the custody system (10) (S4001).

[0133] The abnormal transaction analysis unit (4120) can use an artificial intelligence model based on deposit / withdrawal data to determine whether the transaction being inspected is an abnormal transaction subject to transaction suspension (S4002).

[0134] If the transaction to be inspected is determined to be an abnormal transaction, the analyzer terminal (4100) can transmit a warning signal to the action terminal (4200) (S4003).

[0135] When the action agent terminal (4200) receives a warning signal, it can output information such as the occurrence of an abnormal transaction and the type of transaction that constitutes an abnormal transaction on an output section, such as a display (S4004). Based on this information, an administrator or action agent using the action agent terminal (4200) can take action on transactions determined to be abnormal.

[0136] Figure 11 is a flowchart of a method of taking action against a consignor terminal (100) determined to be an abnormal transaction according to an embodiment of the present invention.

[0137] Referring to FIG. 11, if the analysis terminal (4100) determines that the transaction subject to inspection is an abnormal transaction, it can transmit a warning signal to the action terminal (4200). At this time, upon receiving the warning signal, the action terminal (4200) can transmit a transaction stop signal to the custody system (10) to stop the transaction subject to inspection (S4101). Upon receiving the transaction stop signal, the custody system (10) can stop the transaction subject to inspection corresponding to the transaction stop signal. In addition, upon receiving the transaction stop signal, the custody system (10) can also stop or sanction other transactions by the consignor terminal (100) involved in the transaction subject to inspection.

[0138] When the action terminal (4200) receives a warning signal, it can output information indicating the occurrence of an abnormal transaction and which transaction is an abnormal transaction, etc., on an output unit such as a display of the action terminal (4200) (S4102).

[0139] Based on this information, an administrator or action taker using the action taker terminal (4200) can take action on transactions determined to be abnormal. For example, the administrator or action taker can precisely analyze whether an abnormal transaction detected through the action taker terminal (4200) is normal. Furthermore, if there are multiple administrators or action takers, the decision-making of the internal monitoring and control mechanism can be performed through the action taker terminals (4200) used by each administrator or action taker. The input unit (4210) can receive the results of the precise analysis or the decision-making results of the internal monitoring and control mechanism from the administrator or action taker (S4103).

[0140] The action terminal (4200) can receive information indicating that the transaction subject to inspection has been confirmed as an abnormal transaction or a normal transaction based on the results of a detailed analysis or a decision by an internal monitoring and control organization (S4104).

[0141] If the action terminal (4200) receives abnormality confirmation information indicating that the transaction subject to inspection is an abnormal transaction after transmitting a transaction stop signal ('Yes' in S4104), the action terminal may transmit a signal to the custody system (10) to indicate that action in accordance with the law is to be taken for the transaction subject to inspection. At this time, if the custody system (10) receives a signal to indicate that action in accordance with the law is to be taken for the transaction subject to inspection, it may proceed with a preset process in accordance with the law for the transaction subject to inspection that was stopped by the transaction stop signal (S4105). In addition, if the custody system (10) receives a signal to indicate that action in accordance with the law is to be taken for the transaction subject to inspection, it may proceed with a preset process in accordance with the law for other transactions of the consignor terminal (100) involved in the transaction subject to inspection.

[0142] After transmitting a transaction suspension signal, if the input unit (4210) receives normal confirmation information indicating that the transaction subject to inspection is a normal transaction ('No' in S4104), the action agent terminal (4200) may transmit a transaction resumption signal to the custody system (10) to cancel the suspension of the transaction subject to inspection. At this time, if the custody system (10) receives the transaction resumption signal, it may resume the progress of the transaction subject to inspection that was suspended by the transaction suspension signal (S4106). In addition, if the custody system (10) receives the transaction resumption signal, it may also resume the progress of other transactions of the consignor terminal (100) involved in the transaction subject to inspection.

[0143] The deposit / withdrawal data receiving unit (4110), the abnormal transaction analysis unit (4120), the machine learning unit (4130), and the input unit (4210) may include any one of multiple processors included in the abnormal transaction detection system (4000). Furthermore, the social network posting classification method according to the embodiments of the present invention described so far and the embodiments to be described hereinbefore may be implemented in the form of a program that can be executed by a processor.

[0144] Here, the program may include program commands, data files, and data structures, either singly or in combination. The program may be designed and produced using machine language code or high-level language code. The program may be specifically designed to implement the aforementioned abnormal transaction detection method, or may be implemented using various functions or definitions that are known and available to those skilled in the art of computer software. The program for implementing the aforementioned abnormal transaction detection method may be recorded on a recording medium readable by a processor. In this case, the recording medium may be a memory (4140) provided in the abnormal transaction detection system (4000).

[0145] The memory (4140) can store a program that performs the operations described above and the operations described below, and the memory (4140) can execute the stored program. In the case where there are multiple processors and memories (4140), they can be integrated into a single chip or provided in physically separate locations. The memory (4140) can include volatile memory such as Static Random Access Memory (S-RAM) and Dynamic Random Access Memory (D-RAM) for temporarily storing data. In addition, the memory (4140) can include nonvolatile memory such as Read Only Memory (ROM), Erasable Programmable Read Only Memory (EPROM), and Electrically Erasable Programmable Read Only Memory (EEPROM) for long-term storage of control programs and control data.

[0146] The processor may include various logic circuits and arithmetic circuits, process data according to a program provided from memory (4140), and generate control signals according to the processing results. The processor and memory (4140) may be provided in a server.

[0147]

[0148] Meanwhile, customers of digital asset custody services may wish to receive services for virtual assets traded through validators that can only deposit virtual assets of a pre-fixed value.

[0149] Ethereum is an example of a virtual asset traded through a validator that only allows deposits of virtual assets of a pre-fixed value. With the Ethereum 2.0 upgrade, the system transitioned from proof-of-work to proof-of-stake, allowing for staking to generate proof-of-stake fees instead of mining. Depositing Ethereum requires a fixed amount of 32 ETH per validator. This means that customers may have difficulty depositing Ethereum that is not divisible by 32 ETH, such as 10 ETH, through a validator.

[0150] This presents a challenge: unless customers deposit all of their relatively expensive, fixed-value virtual assets, they have difficulty accessing services for virtual assets traded through validators that only accept pre-determined virtual assets. Therefore, a staking service is needed to reduce the burden of operating a validator for customers who wish to deposit small amounts of virtual assets.

[0151] FIG. 12 is a drawing for explaining asset staking through a custody system in a staking service providing system according to an embodiment of the present invention, and FIG. 13 is a drawing illustrating a staking service providing system through a custody system according to an embodiment of the present invention.

[0152] Referring to FIGS. 12 and 13, the staking service provided by the staking service providing system according to one embodiment can be divided into a solo staking service and a pool staking service.

[0153] For example, a customer wishing to deposit Ethereum could stake the entire 32 ETH as a solo staking service. Alternatively, a pool staking service could be provided, allowing multiple customers to pool 32 ETH with small deposits and then stake them to share the interest.

[0154] The staking service provision system may be installed on the server of a digital asset custody service provider, but the staking service provision system is not necessarily limited to being installed on the service provider's server.

[0155] The staking service provision system may include a custody system (10), a smart contract (400), and a staking contract (5100).

[0156] The custody system (10) provides a service for managing virtual assets subject to transactions through validators, and allows deposits of a pre-fixed value of the virtual asset subject to transactions per validator. The virtual asset subject to transactions may be a type of virtual asset, such as Ethereum, that allows only a pre-fixed value to be deposited per validator.

[0157] The smart contract (400) can receive a transaction fee from the custody system (10) through a payment account (12) and, based on a signal received from the consignor terminal (100), perform a transaction of the virtual asset corresponding to the consignor terminal (100). The smart contract (400) can receive a signal from the consignor terminal (100) indicating that the consignor terminal wishes to deposit the virtual asset to be traded.

[0158] Based on the signal received by the smart contract (400) from the consignor terminal (100), the staking contract (5100) can deposit a virtual asset to be traded at a pre-fixed value per validator.

[0159] FIG. 14 is a diagram for explaining generating verifier deposit data and registering a verifier according to an embodiment of the present invention.

[0160] Referring to FIG. 14, the staking service providing system can be configured to generate validator deposit data through a node server before depositing with a staking contract (5100) and to prepare a validator by registering the validator with the node.

[0161] The consignor terminal (100) can transmit a staking request signal indicating that it wants to use the staking service to the custody system (10).

[0162] When the custody system (10) receives a staking request signal from the consignor terminal (100), it can generate validator deposit data through the node of the virtual asset to be traded.

[0163] The custody system (10) can register a validator with a node of a virtual asset to be traded based on validator deposit data. At this time, the custody system (10) can store the validator deposit data in a custody database (5200). The custody database (5200) may be a database that stores validator deposit data corresponding to the consignor terminal (100) of a customer using the staking service.

[0164] The verifier deposit data may include a keystore file and deposit data.

[0165] When the custody system (10) receives a staking request signal from the consignor terminal (100), it can generate a keystore file and deposit data.

[0166] Thereafter, the custody system (10) can transmit the keystore file to the node of the virtual asset to be traded. At this time, the custody system (10) can transmit the deposit data to the consignor terminal (100).

[0167] Referring to FIG. 13, FIG. 13, and FIG. 14, when the study system receives a staking request signal from any one of the consignor terminals (100), it can determine whether the value of the virtual asset to be traded for which the consignor terminal (100) requested a deposit corresponds to a value fixed in advance for each verifier.

[0168] If the value of the virtual asset to be traded, for which a deposit has been requested by a single entrustor terminal (100), corresponds to a pre-fixed value per verifier, the custody system (10) can generate verifier deposit data corresponding to the corresponding entrustor terminal (100). At this time, the custody system (10) can register the verifier corresponding to the corresponding entrustor terminal (100) to the node of the virtual asset to be traded.

[0169] The custody system (10) can combine the consignor terminals (100) so that the sum of the values ​​of the virtual assets to be traded requested by at least one other consignor terminal (100) becomes a pre-fixed value for each verifier, by adding the values ​​of the virtual assets to be traded requested by at least one other consignor terminal (100) if the value of the virtual assets to be traded requested by one consignor terminal (100) is less than a pre-fixed value for each verifier.

[0170] At this time, the custody system (10) can generate a unique number corresponding to a plurality of consignor terminals (100) combined so that the sum of the values ​​of the requested transaction target virtual assets becomes a pre-fixed value for each verifier.

[0171] When a unique number corresponding to a plurality of combined consignor terminals (100) is generated, the custody system (10) can generate verifier deposit data corresponding to one unique number and register the verifier corresponding to one unique number to the node of the virtual asset to be traded.

[0172] In this way, the staking service provision system according to one embodiment can provide a virtual asset deposit service to customers who wish to deposit a virtual asset for a transaction whose value is less than a pre-fixed value per verifier by having one verifier correspond to a group that is grouped by one unique number by combining the virtual asset for a transaction whose value is requested to be deposited by one entrustor terminal (100) with the virtual asset for a transaction whose value is requested to be deposited by another entrustor terminal (100).

[0173] FIG. 15 is a diagram for explaining staking with a proxy account according to an embodiment of the present invention.

[0174] Referring to Figure 15, it is possible to confirm the process of executing staking with the assets deposited by the customer in the smart contract (400) through the payment account together with the validator deposit data.

[0175] When the custody system (10) receives a staking request signal from the consignor terminal (100), it can execute staking of the asset deposited in the smart contract (400) together with the verifier deposit data by transferring the virtual asset to be traded, deposited in the smart contract (400) by the consignor terminal (100) to the smart contract (400) through the payment account (12) along with the verifier deposit data.

[0176] FIG. 16 is a diagram for explaining transferring assets to a customer by unstaking according to an embodiment of the present invention.

[0177] Referring to Figure 16, some or all customers using the staking service can request unstaking when they wish to withdraw their deposits. Once staking is completed, the customer's principal and interest can be deposited into the payment account (12). At this time, a fee is collected from the interest and the principal and interest can be distributed to each customer.

[0178] When the smart contract (400) receives a withdrawal request signal and deposit data from the entrustor terminal (100), the staking contract (5100) can be configured to deposit the principal and interest of the verifier corresponding to the entrustor terminal (100) into the payment account (12).

[0179] Meanwhile, if a customer who wishes to unstake is a customer who has deposited a small amount of the virtual asset to be traded through a verifier corresponding to their unique number, a separate unstaking procedure may be required.

[0180] The staking contract (5100) may be configured to deposit the principal and interest of the verifier corresponding to the unique number into the payment account (12) when the smart contract (400) receives a withdrawal request signal and deposit data from one of the consignment terminals (100) corresponding to the unique number after a unique number corresponding to a plurality of consignment terminals (100) is generated.

[0181] At this time, the smart contract (400) can be configured to distribute the principal and interest deposited into the payment account (12) to the consignor terminals (100) corresponding to the unique numbers.

[0182] Fig. 17 is a flowchart of a control method of a staking service providing system according to an embodiment of the present invention.

[0183] Referring to Figure 17, the smart contract (400) can receive a signal from the consignor terminal (100) indicating that the consignor wishes to deposit the virtual asset to be traded (S5001).

[0184] When the custody system (10) receives a staking request signal from the consignor terminal (100), it can generate validator deposit data through the node of the virtual asset to be traded. At this time, the custody system (10) can register a validator to the node of the virtual asset to be traded based on the validator deposit data (S5002).

[0185] When the custody system (10) receives a staking request signal from the consignor terminal (100), the consignor terminal (100) transfers the virtual asset to be traded, deposited in the smart contract (400) by the consignor terminal (100) to the smart contract (400) together with the verifier deposit data through the payment account (12), thereby executing staking of the asset deposited in the smart contract (400) together with the verifier deposit data (S5003).

[0186] When the smart contract (400) receives a withdrawal request signal and deposit data from the entrustor terminal (100), the staking contract (5100) can be configured to deposit the principal and interest of the verifier corresponding to the entrustor terminal (100) into the payment account (12).

[0187] At this time, if the staking contract (5100) generates a unique number corresponding to a plurality of combined consignor terminals (100), and the smart contract (400) receives a withdrawal request signal and deposit data from one of the consignor terminals (100) corresponding to the unique number, the smart contract (400) may be configured to deposit the principal and interest of the verifier corresponding to the unique number into the payment account (12).

[0188] At this time, the smart contract (400) can be configured to distribute the principal and interest deposited into the payment account (12) to the consignor terminals (100) corresponding to the unique numbers.

[0189]

[0190] Figure 18 is a flowchart of a withdrawal method using a side chain based on a custody system according to one embodiment.

[0191] Referring to FIG. 18, a withdrawal method using a side chain based on a custody system according to one embodiment may include a step of creating a multi-party computation wallet (S3210), a step of allocating key shares (S3220), a step of depositing funds into a staking wallet (S3230), a step of requesting a signature from a user (S3240), a step of receiving signature data (S3250), and a step of performing asset transfer of the staking wallet (S3260). Although FIG. 18 illustrates steps S3210 to S3260 being performed sequentially, this is not limited thereto, and some steps may be combined and performed simultaneously, some steps may be omitted, or new steps may be added.

[0192] The step (S3210) of creating a multi-party computation wallet may be a step of creating a multi-party computation (MPC) wallet capable of depositing funds at the customer's request. For example, the processor of the custody system may create N multi-party computation wallets for each of N customers. However, this is not limited to this, and two or more multi-party computation wallets may be created for a single customer.

[0193] The processor can allocate keyshares to multi-party computation wallets (S3220). At this time, only one keyshare can be allocated to each multi-party computation wallet. In other words, the processor can allocate one keyshare per multi-party computation wallet. This prevents assets in staking wallets from being transferred at the discretion of customers with high asset ratios.

[0194] The step of depositing funds into a staking wallet (S3230) may be a step where the processor transfers assets from a multi-party computing wallet to the staking wallet at the customer's request. Typically, staking requires a minimum staking amount. Therefore, a method of pooling funds from multiple users and allowing them to participate in staking may be applied to achieve this minimum staking amount. Therefore, depositing funds into a staking wallet may involve transferring funds from multiple multi-party computing wallets.

[0195] The step (S3240) of requesting a user's signature may be a step where the processor requests the user's signature to consent to the transfer of assets from the staking wallet. The processor may transmit a signal related to the signature request to the user's terminal. The user may transmit the signature data to the custody system by pressing a button on the user's terminal, for example.

[0196] The step of receiving signature data (S3250) may be a step in which the custody system receives signature data from a user terminal. At this time, the signature data may be generated using a keyshare assigned to a multi-party computation wallet. Specifically, the signature data may be authenticated by being generated using the keyshare. Furthermore, the signature data may include information indicating that it is the signature of a party authorized to sign using the keyshare. The signature data may be data related to the approval generated using the keyshare, rather than the keyshare itself.

[0197] The step (S3260) of transferring assets from a staking wallet may be a step of sending or withdrawing assets based on the signature data received in step S3250. Specifically, the processor may perform the asset transfer from the staking wallet if the consent (signature) of the user requesting staking exceeds the signature threshold. The signature threshold may be set by the consent of at least one user of a multi-party computing wallet. Specifically, the signature threshold may be a value specified in the staking contract. However, the signature threshold may be changed at any time based on user agreement. For example, the signature threshold may be 100%, 50%, 30%, etc., depending on the situation. For example, in the case of staking termination, the signature threshold may be 100%, but is not limited thereto.

[0198]

[0199] Figure 19 is a diagram explaining a withdrawal method using a side chain based on a custody system.

[0200] Referring to Figure 19, the processor can create wallets A, B, and C. Furthermore, the processor can assign a keyshare to each of A, B, and C. Upon receiving a signal related to an asset custody request from a user terminal, the processor can deposit assets from each wallet into a staking wallet to escrow the assets. The staking wallet deposits digital assets into a node, and the node can pay interest on the deposited assets. The interest paid can be allocated in proportion to the amount of assets escrowed by each wallet. Accordingly, the staking wallet can distribute profits to each wallet based on the amount of assets escrowed.

[0201] At this time, transferring assets from a staking wallet requires obtaining signatures from users of Wallets A, B, and C. Users of each wallet can then transmit the signature data generated using their keyshares to the custody system via their user terminals. The custody system can then transfer the staking assets if the number of received signatures exceeds the signature threshold. Therefore, since asset transfers require the consent of users with a single keyshare, secure staking asset management is ensured.

[0202]

[0203] The embodiments described above may be implemented using hardware components, software components, and / or a combination of hardware components and software components. For example, the devices, methods, and components described in the embodiments may be implemented using one or more general-purpose computers or special-purpose computers, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing instructions and responding.

[0204] A processing device can execute an operating system and one or more software applications running on the operating system. Furthermore, the processing device can access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing device is sometimes described as being used singly; however, those skilled in the art will understand that the processing device can include multiple processing elements and / or multiple types of processing elements.

[0205] For example, a processing device may include multiple processors, or a processor and a controller. Other processing configurations, such as parallel processors, are also possible. Software may include computer programs, code, instructions, or a combination of one or more of these, which may configure a processing device to perform a desired operation or may independently or collectively command a processing device.

[0206] The software and / or data may be embodied in any type of machine, component, physical device, virtual equipment, computer storage medium, or device for interpretation by a processing device or for providing instructions or data to the processing device. The software may also be distributed over networked computer systems and stored or executed in a distributed manner. The software and data may be stored on one or more computer-readable recording media.

[0207] The method according to the embodiment may be implemented in the form of program commands that can be executed by various computer means and recorded on a computer-readable medium. The computer-readable medium may include program commands, data files, data structures, etc., either singly or in combination. The program commands recorded on the medium may be those specifically designed and configured for the embodiment or may be known and usable by those skilled in the art of computer software.

[0208] Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical media such as CDROMs and DVDs; and hardware devices specifically configured to store and execute program instructions, such as ROMs, RAMs, and flash memories. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

[0209] Although the embodiments have been described with limited examples and drawings, those skilled in the art will appreciate that various modifications and variations can be made based on the above teachings. For example, appropriate results can be achieved even if the described techniques are performed in a different order than described, and / or components of the described systems, structures, devices, circuits, etc. are combined or combined in a different manner than described, or are replaced or substituted with other components or equivalents. Therefore, other implementations, other embodiments, and equivalents of the claims also fall within the scope of the claims described below.

Claims

1. A withdrawal method using a side chain based on a custody system performed by at least one processor, Steps to create at least one multi-party computation wallet; A step of allocating a key share to each of the at least one multi-party operation wallets; A step of receiving a request for asset custody from a user of at least one multi-party operation wallet and depositing the assets of at least one multi-party operation wallet into a staking wallet; A step of requesting a signature from a user of at least one multi-party computation wallet regarding the transfer of assets of the staking wallet; A step of receiving signature data from a user of at least one multi-party operation wallet; and If the received signature data is greater than the signature threshold, a step of performing asset transfer of the staking wallet is included. A withdrawal method using a side chain based on a custody system.

2. In paragraph 1, The step of depositing the above assets into the staking wallet is performed when the assets collected from at least one multi-party computation wallet are greater than the minimum staking amount. A withdrawal method using a side chain based on a custody system.

3. In paragraph 1, Further comprising a step of paying interest to at least one multi-party computation wallet based on the size of assets deposited in the staking wallet. A withdrawal method using a side chain based on a custody system.

4. In paragraph 1, The above signature threshold is set by the consent of the users of at least one multi-party computation wallet. A withdrawal method using a side chain based on a custody system.

5. In paragraph 1, The step of allocating the above key share is a step of allocating one key share per multi-party computation wallet. A withdrawal method using a side chain based on a custody system.

6. In paragraph 1, The above signature data is generated using the key share assigned to the multi-party computation wallet. A withdrawal method using a side chain based on a custody system.

7. A non-transitory computer-readable recording medium having recorded thereon a program for executing a withdrawal method using a side chain based on the custody system described in paragraph 1.

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