28 results about "Data outsourcing" patented technology

The invention discloses an unbalanced privacy set intersection method suitable for an untrusted cloud. The client C has a set X = {x1,..., xn}, the server S has a private set Y = {y1,..., yN}, and the untrusted cloud server H has no input, nlt; lt; the method comprises the following steps: (1) initializing a system; and (2) data blinding. And (3) data outsourcing. And (4) the untrusted cloud and the server side carry out interactive calculation. And (5) calculating an intersection. According to the method, firstly, an efficient casual privacy equivalence test protocol is designed; by encoding the set elements and combining with the casual transmission extension protocol, the cloud server and the service provider can carry out equality test on the set elements in a secrecy manner. And secondly, blind processing is carried out on the private set, and the main calculation task of the client C is outsourced to the untrusted cloud H, so that the calculation burden of the client C is reduced. And finally, by introducing a Pedersen commitment and a zero-knowledge proof mechanism, possible malicious behaviors of the untrusted cloud H can be resisted, and the security of the system is enhanced.

The invention discloses a random key encryption deduplication method supporting continuous updating, and in order to enable a client to safely outsourced private data to a server, the method comprises the following steps: after initializing system parameters, the client selects a random key to encrypt the data, and sends the encrypted data to the server; and the server side performs repeated check and duplicate removal on the data of the user. And the server verifies the ownership of each uploader to the file through a challenge response mechanism to realize effective ownership management control. According to the method and the device, the user is supported to replace the file key, all legal users can obtain the key updated for multiple times, and the risk of privacy data leakage after key leakage is reduced. Due to the randomness of key selection of the client, the method can resist violent attacks introduced by deterministic convergence encryption. Compared with an encryption deduplication method assisted by a third-party independent server, the method is simpler in deployment. The method is used in the field of encryption deduplication and can provide data privacy.

The invention discloses a privacy protection Skyline query method based on mixed secret sharing under cloud computing, and belongs to the field of cloud data security query. The method comprises the following steps: firstly, establishing a multi-dimensional data outsourcing service scene comprising a cloud server group, a data provider and a client; a data provider holds an original database, and data is sent to a cloud server group after being subjected to offline preprocessing; and then, the client locally encrypts a plaintext query into two secret shares through arithmetic secret sharing, the two secret shares are sent to the cloud server group, the communication overhead is transferred to an offline stage through a function secret sharing technology so as to realize efficient and safe skyline query, and then respective query result sets closest to private plaintext data are obtained. And finally, the cloud server group returns the stored result set to the client, and sums the two shares locally according to the arithmetic sharing property to reconstruct a plaintext result. According to the method, the privacy protection Skyline query efficiency in an actual wide area network environment is improved.

The application discloses a kind of unmanned plane identity anonymization and safe data outsourcing method and system, method is based on integrated supervision platform and unmanned plane, including system initialization stage, integrated supervision platform generates main private key and public parameter;User anonymous and private key generation stage, unmanned plane registers anonymous identity and obtains user private key;Data preprocessing and outsourcing stage, unmanned plane divides data into block and sub-block and encrypts, generates coding vector using erasure code coding, generates verification label for each coding data block, constructs random distribution table according to the number of cloud server, distributes coding vector and label to multiple cloud server storage.In this way, the unmanned plane identity anonymization and safe data outsourcing method described reduces storage redundancy, ensures that data can be reconstructed when multiple servers fail, and guarantees data availability and robust recovery.

The invention belongs to the technical field of electric digital data processing, and particularly relates to a multi-attribute fusion shortest path verifiable query method and device. According to the method, a compact encryption index structure is constructed to support efficient splicing of path segments, a pairing verification mechanism is designed to achieve verifiability of a result, and safe, efficient and credible query of the multi-attribute constraint path is completed on the premise that a graph structure and query intention are not leaked. Through collaborative design of key mechanisms such as structured indexing, fusion encryption, path verification and attribute optimization, the bottleneck of the prior art in terms of query efficiency and verification capability is broken through, personalized path selection and lightweight deployment under multi-attribute constraints are also realized, and the method has wide application prospects and is worthy of popularization and application. The method is suitable for various actual scenes such as cloud data outsourcing, privacy sensitive graph mining and trusted path query.

The invention discloses an anti-collusion privacy protection data training method and a data outsourcing training method, and the method comprises the steps: carrying out the parameter initialization of a to-be-trained model through a server, and generating a public parameter; each client generates a client key pair based on the public parameters, and deploys a client private key in the client key pair in a client enclave of the server; generating a random mask matrix; the server generates a target public key according to a public key parameter and a public parameter in the client key pair; each client encrypts the local training data by using the target public key to obtain an initial ciphertext vector; performing secondary encryption on the initial ciphertext vector based on the random mask matrix to obtain a client ciphertext vector; the server aggregates the ciphertext vectors of the clients to obtain an aggregated ciphertext vector; each client enclave processes the aggregated ciphertext vector based on a client private key to obtain an intermediate vector; and updating the to-be-trained model based on the intermediate vector. The anti-collusion attack is realized, and the risk of sensitive data leakage is reduced.

The invention discloses an efficient fine-grained access control method for fog computing, and relates to the technical field of access control. According to the method, in a cloud-fog cooperative computing architecture, a data owner entrusts ciphertext storage to a cloud storage platform, enjoys elastic storage service at low cost, and outsources partial decryption operation to a fog node, so that an authorized data acquirer can obtain data access service through lightweight operation. Therefore, a CP-ABE mechanism supporting double-stage decryption is innovatively designed, and a private key sk of an original CP-ABE is divided into two parts, namely a conversion key and a decryption key, based on a key splitting idea. The most resource-intensive strategy matching operation is executed by the fog node, and the data acquirer terminal only needs to carry out lightweight final decryption operation. The whole access control process comprises seven stages of system initialization, key generation, data outsourcing, request initiation, data retrieval, primary decryption and secondary decryption. According to the method, the problem of coarse-grained and high-cost data access privacy protection when cloud computing faces massive ciphertext sharing scenes and diversified retrieval requirements of users in the existing access control technology is effectively solved, the dependence on high computing and decryption overhead is avoided, and safe outsourcing part decryption and fine-grained access control are realized.

The present invention relates to a method for additive homomorphic encryption and cryptographic accumulator calculation. The present invention proposes a verifiable outsourced calculation method for graph data based on additive homomorphism and bilinear map accumulators. First, homomorphic cryptography is used to encrypt graph data instantiated by an adjacency list. Additive homomorphic encryption and obfuscation circuits are combined to implement a breadth-first Dijkstra algorithm based on a priority queue to calculate the exact shortest path on the encrypted graph data. Second, a binomial heap structure is constructed based on the encrypted graph data, and a bilinear map accumulator is used to calculate verification evidence for the encrypted shortest path result, thereby achieving verifiable correctness of the outsourced shortest path query calculation result for the encrypted graph data. In addition, the scheme also uses certificateless short signatures to ensure the authenticity and non-tamperability of the shortest path query result and verification result of the cloud server, thereby achieving probabilistic correctness verification of the outsourced calculation result of the shortest path query for encrypted graph data.

The present invention discloses a data outsourcing computing transaction method based on smart contracts, comprising: deploying smart contracts on a blockchain platform; a client joins the blockchain and calls the smart contract; and the client deposits a deposit according to the client deposit amount in the smart contract; the client publishes the transaction on the blockchain platform, and multiple service ends participating in the transaction join the blockchain and call the smart contract, and deposit deposits respectively according to the server deposit amount in the smart contract; the client transmits the data to be calculated to multiple service ends respectively based on an oblivious transfer protocol through the smart contract; the service end calculates with the received data to be calculated and the data calculation method, and transmits the calculation result to the smart contract; the smart contract verifies the calculation result received from each service end, and if the verification is successful, the client receives the calculation result through the smart contract and pays the service end through the smart contract; if the verification fails, the deposit of the corresponding service end is deducted.

The application discloses a kind of cloud computing-oriented security outsourcing data processing method and device.Wherein, the method includes: by public key, target data is encrypted and handled, obtains first ciphertext data, and first ciphertext data is sent to first cloud server;First ciphertext data is converted from decimal to centesimal, obtains second ciphertext data;Second ciphertext data is calculated to the integer part of the square root result, and obtains the integer part one of the square root result of second ciphertext data;Second ciphertext data is calculated to the floating point number part of the square root result, and obtains the floating point number part of the square root result of second ciphertext data;According to integer part one and floating point number part, obtain the square root result of target data.The application solves the technical problem that existing security outsourcing cloud computing mode cannot support the calculation of arithmetic square root, so that cloud computing has great limitation in data outsourcing scene.

The application belongs to the technical field of information security, and provides a layered blockchain data outsourcing sharing method and system for Internet of Things devices, wherein the layered blockchain data outsourcing sharing method for Internet of Things devices comprises the following steps: a key generation center acquires public parameters and generates a system master key and a master public key based on the public parameters; the key generation center generates a user private key and a user public key based on a user ID, and a user end verifies based on the user private key to obtain a first verification result; a data holding end acquires sharing parameters and holding data, generates encrypted data based on the sharing parameters and the holding data, and generates a calculation label based on the encrypted data; when a data request end belongs to a shareable authorized list, a proxy node generates conversion ciphertext based on an encryption key and a public key of the data request end, and the data request end generates plaintext based on the conversion ciphertext and a public key of the sharing parameters, so that the data is outsourced and shared under the premise of ensuring privacy.

The application provides a homomorphic encryption-based plaintext data outsourcing encryption system and method. The outsourcing encryption system comprises a data provider and an encryption and decryption platform. The data provider comprises a data storage unit, a random number generation unit, a confusion unit, a data provider side communication unit and a de-confusion unit. The encryption and decryption platform comprises a platform side communication unit and a homomorphic encryption unit. The data storage unit stores plaintext data to be encrypted. The random number generation unit generates random numbers. The confusion unit uses the random numbers to perform confusion processing on the plaintext data to obtain plaintext confusion data. The data provider side communication unit sends the plaintext confusion data to the encryption and decryption platform. The homomorphic encryption unit uses an encryption key to perform homomorphic encryption on the plaintext confusion data to obtain ciphertext confusion data. The platform side communication unit sends the ciphertext confusion data to the data provider. The de-confusion unit uses the random numbers to perform de-confusion processing on the ciphertext confusion data to obtain ciphertext data.

The invention discloses an elevator paperless maintenance certification method based on a block chain, and the method is characterized in that the method comprises the steps: S1, data outsourcing mixed storage: constructing a merkle tree of file fragments, and encrypting the merkle tree; s2, accessing and controlling the intelligent contract file, and fusing an RBAC model on the basis of the Ethereum intelligent contract technology to form an access control model; s3, an elevator paperless maintenance system combining the block chain and the Hash algorithm is established; the paperless elevator maintenance system is provided with a plurality of user interfaces, and elevator supervision in the whole life cycle is achieved. According to the system, landing implementation of paperless maintenance is achieved, 'whole-process supervision 'can be achieved, a data file is generated, management of operating personnel, inspection personnel and maintenance personnel is also brought into all supervision links, the operating personnel are on duty with certificates, the operation process is standardized, the whole life cycle of elevator equipment is accurately managed, and all-around big data statistical analysis is constructed.

The application discloses a random key encryption deduplication method supporting continuous updating. In order to enable a client to safely outsource private data to a server, the method comprises the following steps: after system parameters are initialized, the client selects a random key to encrypt data and sends the encrypted data to the server. The server checks and deduplicates the data of the user. The server verifies the ownership of the file of each uploader through a challenge-response mechanism to realize effective ownership management control. The application supports the replacement of the file key of the user and can guarantee that all legal users obtain the key updated for multiple times, thereby reducing the risk of private data leakage after the key leakage. Due to the randomness of the key selected by the client, the application can resist the brute force attack introduced by the deterministic convergent encryption. Compared with the encryption deduplication method assisted by a third-party independent server, the method is simpler in deployment. The application can provide data privacy in the field of encryption deduplication.

The invention relates to a multi-attribute secret state data efficient matching and self-adaptive task allocation method and system supporting dynamic updating, and the method comprises the steps: a data outsourcing step: a crowdsourcing platform constructs an index tree fusing multiple attributes of space, time and keywords, encrypts the index tree, and outsources the encrypted index tree to a cloud server; a privacy protection query step: a worker submits an encrypted query token, and the double cloud servers cooperatively traverse the index tree in a ciphertext state by executing a security comparison protocol and a security subset query protocol; and a dynamic updating step, wherein the double cloud servers support operations such as task insertion, task deletion and node splitting on the index tree in a ciphertext state. According to the crowdsourcing task allocation method, comprehensive privacy protection on the task data and the query request is realized in an untrusted cloud environment, meanwhile, efficient multi-attribute joint retrieval and dynamic data updating are supported, and the problems of single privacy protection dimension, low calculation efficiency and insufficient dynamic updating support in an existing crowdsourcing task allocation scheme are effectively solved.

This invention describes a privacy-preserving keyword-based similarity query method for smart healthcare. This method is based on a typical medical data outsourcing system scenario. This outsourcing system includes three participants: a treatment center, a querying user, and a cloud server. The outsourcing system also includes six modules: 1) a system initialization module, 2) a data organization module, 3) a data encryption module, 4) a query trapdoor generation module, 5) a query service module, and 6) a data decryption module. Based on in-depth research and analysis of existing privacy-preserving technologies and research results for similarity queries and keyword matching, this invention implements a secure and efficient keyword-based multidimensional similarity query solution and application system for smart healthcare.

The embodiments of the present application provide a searchable encryption method, computer device, storage medium, and computer program product, relating to the field of searchable encryption technology. The method comprises: a cloud server receives a data outsourcing request from a data provider; sends a keyword set ciphertext to a trusted execution environment; the trusted execution environment uses a keyword set key to obtain a keyword set plaintext; the cloud server forwards a data retrieval request to the trusted execution environment; the trusted execution environment uses a private key in a first key pair to decrypt the combined ciphertext to obtain the keyword to be retrieved and a re-encryption key; the trusted execution environment obtains the file data to be retrieved based on the keyword to be retrieved; the trusted execution environment uses the re-encryption key to obtain the re-encrypted file data and sends the re-encrypted file data to a data acquirer; the data acquirer uses the re-encryption key to obtain the file data to be retrieved. This method improves data retrieval efficiency and supports fuzzy search of data.

The invention discloses an unmanned aerial vehicle identity anonymization and security data outsourcing method and system, and the method is based on a comprehensive supervision platform and an unmanned aerial vehicle, and comprises the steps: a system initialization stage: the comprehensive supervision platform generates a main private key and public parameters; in a user anonymous and private key generation stage, an unmanned aerial vehicle registers an anonymous identity to obtain a user private key; in the data preprocessing and outsourcing stage, the unmanned aerial vehicle divides data into blocks and sub-blocks, encrypts the blocks and the sub-blocks, encodes the blocks and the sub-blocks by using erasure codes to generate encoding vectors, generates verification labels for each encoding data block, constructs a random distribution table according to the number of the cloud servers, and allocates the encoding vectors and the labels to a plurality of cloud servers for storage. Therefore, through the unmanned aerial vehicle identity anonymization and security data outsourcing method, storage redundancy is reduced, it is ensured that data can be reconstructed when multiple servers fail, and data availability and robust recovery are guaranteed.

This invention belongs to the field of electronic digital data processing technology, specifically relating to a method and apparatus for verifying shortest paths with multi-attribute fusion. The method constructs a compact encrypted index structure to support efficient path segment concatenation and designs a pairing verification mechanism to ensure the verifiability of the results. Without revealing the graph structure or query intent, it achieves secure, efficient, and reliable querying of multi-attribute constrained paths. Through the collaborative design of key mechanisms such as structured indexing, fusion encryption, path verification, and attribute optimization, it not only overcomes the bottlenecks of existing technologies in query efficiency and verification capabilities but also realizes personalized path selection and lightweight deployment under multi-attribute constraints. It has broad application prospects and is suitable for various practical scenarios such as cloud data outsourcing, privacy-sensitive graph mining, and trusted path querying.

The invention provides a feature selection method based on secure multi-party computing, which is technically characterized in that the execution of a computing task is completed by a server by using a secure multi-party computing technology and adopting a data outsourcing form, so that the resource consumption of a user side is reduced, the feature score of a data set feature is obtained by using various secure multi-party computing technologies, and the user experience is improved. And further, a comparison vector is obtained through cyclic comparison, and a one-hot matrix is obtained by adopting a lookup table technology, so that a selected feature matrix is obtained, the calculation complexity of a feature selection process is reduced, feature selection under privacy protection is realized, and the problem that the feature selection process has relatively high complexity is solved.

The application provides a ciphertext data outsourcing decryption system and method based on homomorphic encryption. The outsourcing decryption system comprises a data application party and a decryption platform. The data application party comprises a data storage unit, a random number generation unit, a confusion unit, a data application side communication unit and a de-confusion unit. The decryption platform comprises a platform side communication unit and a homomorphic decryption unit. The data storage unit stores ciphertext data. The random number generation unit generates random numbers. The confusion unit uses the random numbers to perform confusion processing on the ciphertext data to obtain ciphertext confusion data based on the characteristic that homomorphic encryption can perform plaintext and ciphertext operation. The data application side communication unit sends the ciphertext confusion data to the decryption platform. The homomorphic decryption unit uses a decryption key to perform homomorphic decryption on the ciphertext confusion data to obtain plaintext confusion data. The platform side communication unit sends the plaintext confusion data to the data application party. The de-confusion unit uses the random numbers to perform de-confusion processing on the plaintext confusion data to obtain plaintext data.

The application discloses a database sensitive data sending behavior marking and traceability tracking system and method, and the system comprises six units, i.e., a data feature perception and acquisition unit, a behavior feature extraction unit of an optimized bat algorithm and the like. The data sending parameters are acquired through the data feature perception and acquisition unit, the behavior features are extracted through the optimized bat algorithm, the state transition is analyzed by improving the Markov chain model, the data marking code is generated, the traceability index is constructed, and the tracking feedback is performed. The method comprises six steps, i.e., data acquisition, feature extraction, state analysis, code generation, index construction and tracking feedback. Through the cooperation of the units of the system and the specific method, the application realizes accurate marking and efficient traceability tracking of the database sensitive data sending behavior, and provides a reliable technical means for data security protection.

The invention discloses an attribute secret state document retrieval method supporting keyword fuzzy search, belongs to the field of data outsourcing storage in data security, and aims to overcome the defects of traditional data outsourcing storage and provide keyword fuzzy query and authority control functions for the traditional data outsourcing storage. The method mainly comprises three parts: in the first part, a natural language processing technology is adopted to perform word segmentation and encryption on keywords of a document, so that the keywords can meet the requirement of fuzzy retrieval; the second part is based on symmetric encryption, and each document is encrypted by adopting a unique symmetric encryption key of the document; in the third part, attribute encryption is used, the symmetric key of the document is encrypted through revocable attribute encryption, and finally, only the user meeting the authority can decrypt the corresponding document plaintext.

The present invention discloses a privacy-preserving Skyline query method based on hybrid secret sharing under cloud computing, belonging to the field of secure query of cloud data. First, a multi-dimensional data outsourcing service scenario including a cloud server group, a data provider, and a client is established. The data provider holds the original database, preprocesses the data offline, and then sends it to the cloud server group. Then, the client encrypts the plaintext query into two secret shares through arithmetic secret sharing locally and sends them to the cloud server group. The communication overhead is transferred to the offline stage through function secret sharing technology to achieve efficient secure Skyline query, and then obtains their respective query result sets closest to the private plaintext data. Finally, the cloud server group returns the stored result set to the client, and the plaintext result is reconstructed by summing the two shares locally according to the nature of arithmetic sharing. The present invention improves the efficiency of privacy-preserving Skyline query in the actual wide area network environment.

The application discloses a non-equilibrium privacy set intersection method suitable for an untrusted cloud. A client C has a set X={x1,...,x n}, a server S has a private set Y={y1,...,y N}, and an untrusted cloud server H has no input, wherein n

The invention belongs to the technical field of information security, and provides a hierarchical block chain data outsourcing and sharing method and system oriented to an internet of things device, and the method comprises the steps that a key generation center obtains a public parameter, and generates a system master key and a master public key based on the public parameter; the key generation center generates a user private key and a user public key based on the user ID, and the user side performs verification based on the user private key to obtain a first verification result; the data holding end obtains the sharing parameter and the holding data, generates encrypted data based on the sharing parameter and the holding data, and generates a calculation label based on the encrypted data; and when the data request end belongs to the sharable authorization list, the proxy node generates the conversion ciphertext based on the encryption key and the public key of the data request end, and the data request end generates the plaintext based on the conversion ciphertext and the public key of the shared parameter, so that outsourcing sharing of the data is realized on the premise of ensuring privacy.

The application discloses a TEE-assisted safe and efficient distributed high-dimensional data similarity query method, which comprises the following steps: constructing a TEE-assisted safe and efficient distributed high-dimensional data similarity query system and initializing the query system; a data owner constructs the data in a data set into a TPSS form and distributes the data to a first cloud server and a second cloud server; a PB tree is constructed by using the distributed data; when an update data is added or deleted in the data set, the first cloud server, the second cloud server and a trusted execution environment search the PB tree by cooperative operation to add or delete the update data; and according to a similarity query request proposed by a query agent, the first cloud server, the second cloud server and the trusted execution environment provide query services for the query agent. The application can support safe query index construction, efficient similarity query processing and data dynamic updating based on distributed high-dimensional data outsourcing.

The present invention discloses a multi-party collaborative public auditing method for privacy protection, which relates to the field of collaborative verification of data integrity and includes three entities: Cloud Service Provider (CSP), Third Party Auditor (TPA), and Department Member Group (MG). The method includes: system initialization phase, distributed key generation phase, data outsourcing phase, and integrity auditing phase. The present invention allows members within a group to form a threshold group to jointly generate a tag for a piece of data, reducing the tag redundancy of the same piece of data; during the multi-party collaboration process, it protects the information privacy of collaborative members. If a certain member is attacked, it will not affect the security of the data tag. Based on secret sharing and MtA conversion technology, the present invention realizes the tag multi-party unorganized collaborative generation without additional communication overhead, resists the "key person attack", and solves the problem of responsibility and right division in the multi-user data outsourcing scenario.