33 results about "Decentralized computing" patented technology

A system and method for providing a randomness function to a smart contact on a decentralized network. An oracle receives a request for at least one random value to be supplied to a smart contract executing on a decentralized network, the request including a seed. The oracle generates the at least one random value based on the seed, and a secret key associated with the oracle, wherein the oracle is identified on the decentralized network by a public key associated with the secret key. The oracle generates a cryptographic proof based on the at least one random value and the public key and sends the at least one random value and the cryptographic proof to the decentralized network, whereby the proof, and thus the randomness of the at least one random value, can be verified.

A computing node in a P2P computer network obtaining a media item to be verified, determining a hash value for the media item based on a digital cryptographic hash function, retrieving a plurality of data records associated with the media item from a blockchain based at least in part on the hash value, wherein a data record is generated by a validation node in the decentralized P2P computer network, evaluating the plurality of data records to determine whether the media item has satisfied pre-defined validation criteria, and providing information describing the media item based at least in part on the plurality of data records, wherein the information provides an indication as to whether the media item satisfied the pre-defined validation criteria.

Disclosed is system for fulfilling service request using computing nodes. The system comprises a decentralized computing network, comprising computing nodes operating as autonomous agents, configured to implement software framework comprising: client-agent device 5 configured to: receive service request and generate objective therefor, and send objective to agent-device configured to send queries to a machine learning model agent and / or obtain tasks, and list of autonomous agents associated with objective; and processing arrangement configured to: generate directed acyclic graph (DAG) having 10 nodes representing first task and edges representing dependency of first task on second task(s) amongst tasks, and initialize Bloom filter of each node to track completion status of second task(s). Autonomous agent is configured to: identify second task(s), check completion status of second task(s), using Bloom filter of first task, upon completion of second 15 task(s), execute first task, and update Bloom filter.

A method and apparatus for secured, peer-to-peer transfer of data rights over a computer network, the method being accomplished by a decentralized computing system including a distributed ledger platform and an off-chain data host platform. A data owner authorizes the minting and distribution of an on-chain authorization token using the distributed ledger. The authorization token includes primary and secondary encryption levels to ensure secure data transfer. The authorization tokens are used to track data access rights, enforce access policies, and control distribution of encryption keys transferred between the data owner, an administrator, a recipient, and / or a delegate.

A system and method in which a network of offchain oracle nodes are used to provide dynamic information to consuming smart contracts in a decentralized computing environment. The off-chain oracle network and a corresponding protocol reduces the likelihood of damage from rogue oracles acting in concert. Periodically, the oracle nodes, jointly and in concert, run the protocol to sign a report containing observations from many of the oracles in the network. The report is transmitted to a smart contract C running on a DLT. The smart contract C can validate the report, pay each oracle node that contributed an observation to the report and expose the median, or other calculated value, of the reported values to the on-chain consuming smart contracts.

The invention relates to a method of encrypted communication. An encrypted connection to a metadata node is established by a subscriber device via two primary router nodes and one secondary router node of a decentralized computing system. A stream of data packets is divided into corresponding parts between the primary router nodes, and the length of each packet transmitted is shorter than the encryption time. At each of the primary router nodes, the IP address of the transmitting subscriber device is substituted by a primary temporary IP address during the routing of the data packets. At the secondary router node, the primary temporary IP address is substituted by a secondary temporary IP address. The metadata node is the closest accessible metadata node to the secondary router node. At the metadata node, a task is divided into sub-tasks, at least two executing nodes are allocated to each sub-task, data relating to the task is received from the transmitting subscriber device, the received data are distributed among the sub-tasks, and the encrypted portion of the distributed data is sent to each of the executing nodes according to the allocation of the sub-tasks. The technical result consists in improving data protection in a distributed data transmission network.

The invention discloses a federated learning-based satellite internet model security data sharing method, which comprises the following steps of: firstly, constructing a self-adaptive ground mobile terminal clustering strategy based on a dynamic graph attention mechanism, and establishing a space-time sensing ring topology by analyzing a node movement rule and communication characteristics to lay a network foundation for decentration calculation; secondly, a modular decomposition confusion encryption scheme is developed, homomorphic encryption of gradient parameters is realized by using a redundant modular component superposition and random number injection technology, the ciphertext size is compressed, and dependence of a third-party center is avoided through distributed private key management. And finally, creating a multi-sub-ring parallel computing architecture, splitting a modular decomposition task into independent computing units, and realizing local model combination based on a proximity communication principle, so that the bottleneck of a traditional server is eliminated, and the aggregation delay is also reduced. According to the whole scheme, through collaborative design of a topology layer, an encryption layer and a calculation layer, feasibility and efficiency balance of cross-domain collaborative training is achieved under the condition that data privacy is guaranteed.

Approaches are described for a decentralized computing platform that enables dynamic execution of distributed services through a hybrid on-chain and off-chain architecture. The platform consists of a network of nodes, each running a virtual machine to host and execute service instances. A sequencer component records, sequences, and notarizes function calls to maintain a consistent service-level state across the network. Computation takes place off-chain on nodes that participate dynamically in specific services based on resource availability. A persistent virtual memory structure manages both service-level and instance-level state. The service-level state is synchronized across nodes participating in the same service, while the instance-level state is isolated to individual nodes for independent operations. The memory system is non-volatile, retaining state across function calls and enabling direct execution from memory. A universal procedure call (UPC) framework facilitates decentralized communication through multicast-enabled operations with customizable fault tolerance and supports applications from finance to high-performance computing.

A method of operating a blockchain system with decentralized computing nodes includes determining an elected clan representing a. subset of the plurality of the decentralized computing nodes that are capable of approving addition of the block to the blockchain with less than or equal to a threshold probability of failure and performing a block approval process which includes determining a vertex comprising a digest of the block, communicating the vertex to each computing node, communicating the block only to the nodes in the elected, clan, while preventing communication of the block to the nodes that are not members of the elected clan, and receiving one or more messages indicating receipt of the vertex and / or receipt of both the vertex and the block.

Methods and systems for blockchain and smart contract-supported decentralized computation are provided. A computation task from a task initiator node is registered on a blockchain by invoking a smart contract, and assigned to an edge computing node within a decentralized computing network, with task details sent by the task initiator node to the edge computing node through a secure peer-to-peer connection. The task initiator node and the edge computing node may select each other using peer discovery methods based on reputation scores. The edge computing node determines a solution to the computation task. The solution may be verified on-chain by the smart contract, or off-chain by the task initiator node, and a token reward may be given to the edge computing node by the smart contract upon solution verification. The edge computing node is incentivized to determine the solution by the reward provided by the smart contract.

Decentralized computer systems and methods are disclosed for hyperledger-based payment transactions, alerts, and dispute settlement, using smart contracts. One method includes: receiving transaction information comprising transaction data of transaction attributes for a transaction for a good or service originating at a merchant; storing the transaction data of the transaction attributes for the transaction, in a Smart Contract generated or updated by a Smart Contract application; deploying the Smart Contract into one or more blockchain networks, wherein at least one of the one or more blockchain networks; performing one or more iterations of enabling, using an inter ledger protocol, to transfer at least some of the transaction data of the transaction attributes of the transaction to another of the one or more blockchain networks, wherein each of the one or more blockchain networks serves one or more business functions of the transaction.

A decentralized scheduler node for an unstable network of heterogenous nodes collects status information including a resource capacity of each of a network of nodes with which it is in communication. For each current computation job in its input queue, the scheduler node determines which connected nodes have suitable resource capacity to execute the current job and a reliability of each suitable node based on statistical evaluation of its historical availability for job deployment. From the set of suitably powerful and reliable candidate nodes, the scheduler node selects a candidate node of sufficient resource capacity for deployment of the current job based on the probability of a subsequent job appearing during the execution time of the current job, the subsequent job having an equivalent or greater resource requirement and thus at risk of waiting in the input queue if the selected node is required for executing the subsequent job.

A client device operates by: reading, via the client device, an optical code associated with a player token; sending, via the client device and in response to reading the optical code, a request to download a player token NFT associated with the player token from an NFT distribution platform implemented as a decentralized computer system having a plurality of different nodes, the player token NFT stored on a blockchain via the plurality of different nodes of the decentralized computer system; receiving from the blockchain of the NFT distribution platform, via the client device, the player token NFT associated with the player token responsive to geographically distinct blockchain calculations performed contemporaneously over the plurality of different nodes of a decentralized computer system; and storing the player token NFT in a wallet associated with the user.

Disclosed are systems and methods for a hybrid cloud-edge computing platform using decentralized networks for solving resource-intensive computation tasks such as machine learning and video processing tasks. In some respects, the cloud-edge computing platform comprises a coordinator cluster and worker nodes partitioned into shards, which include cloud-based networks and edge networks, and which cater to a wide range of computation requirements. The coordinator cluster manages workloads by selecting the appropriate shard and worker nodes to execute tasks based on computed efficiency scores. Worker nodes may be incentivized with rewards such as cryptocurrency tokens for successful completion of tasks. The disclosed architecture also supports the pipelining of multiple tasks in a workload, where the output of one task is used as input for another task. In some respects, the computing architecture includes a blockchain-supported edge network, which enables verifying compute tasks and managing reward distribution for worker nodes using smart contracts.

To provide a decentralized processing system, method and storage medium that are accompanied by decentralization of computation tasks to one or more untrusted worker computer systems.SOLUTION: In a method, when an untrusted worker computer system executes calculation for a request computer system, the request computer system (or another verification request computer system) provides information enabling the request computer system to cryptographically verify that the task is completed correctly, the worker computer system provides information including proof and I / O data after the calculation is completed, and the request computer system uses setting, proof and I / O data of open verification key parameters to verify that the calculation executed by the untrusted worker computer system is correct, the calculation executed by a worker being related to a block chain transactions.SELECTED DRAWING: Figure 2

Approaches are described for a decentralized computing platform that enables dynamic execution of distributed services through a hybrid on-chain and off-chain architecture. The platform consists of a network of nodes, each running a virtual machine to host and execute service instances. A sequencer component records, sequences, and notarizes function calls to maintain a consistent service-level state across the network. Computation takes place off-chain on nodes that participate dynamically in specific services based on resource availability. A persistent virtual memory structure manages both service-level and instance-level state. The service-level state is synchronized across nodes participating in the same service, while the instance-level state is isolated to individual nodes for independent operations. The memory system is non-volatile, retaining state across function calls and enabling direct execution from memory. A universal procedure call (UPC) framework facilitates decentralized communication through multicast-enabled operations with customizable fault tolerance and supports applications from finance to high-performance computing.

Distributed denial of service (DDoS) attacks launched using internet of things (IoT) devices may be detected and addressed using decentralized computing, potentially in combination with blockchain or other decentralized ledger technology. One method of doing this may include identifying an anomaly by comparing traffic data against baseline traffic data, reporting an anomaly as a potential attack, receiving an indication that the anomaly is an attack, and then performing a set of response measures. The set of response measures may comprise allocating a dedicated frequency channel to traffic associated with the attack and assigning devices associated with the attack to that channel, while assigning other user devices to different frequency channels. Records of the attack and associated devices may be recorded and propagated across nodes, thereby enabling each node to respond appropriately even as the device moves from its original location. Machine learning can be implemented to improve the analysis of key performance indicators to determine which indicators are most predictive of DDoS attacks.

A method and apparatus for secured, peer-to-peer transfer of data rights over a computer network, the method being accomplished by a decentralized computing system including a distributed ledger platform and an off-chain data host platform. A data owner authorizes the minting and distribution of an on-chain authorization token using the distributed ledger. The authorization token includes primary and secondary encryption levels to ensure secure data transfer. The authorization tokens are used to track data access rights, enforce access policies, and control distribution of encryption keys transferred between the data owner, an administrator, a recipient, and / or a delegate.

Methods, systems, and apparatus including computer programs encoded on computer storage media for implementing a decentralized social-media network. The system platform servers of a social-media platform and a stand-alone client, instances of which are downloadable to and executable on devices of end users to interface with the platform servers. The servers have computer-executable instructions to compute a measure of reputation based on engagements within a community, including moderation work of community content, and based on highly idiosyncratic community-specific rules. The stand-alone client has computer-executable instructions to generate and store a public-private key pair, which is accomplished to the exclusion of the servers. The key pair enables recording of the measure of reputation and related changes on decentralized computing resources in a way that is verifiable. The system can port the measure of reputation off-platform in a way that ensures data coherency and data fidelity amongst data stores of the system.

Disclosed are systems and methods for a hybrid cloud-edge computing platform using decentralized networks for solving resource-intensive computation tasks such as machine learning and video processing tasks. In some respects, the cloud-edge computing platform comprises a coordinator cluster and worker nodes partitioned into shards, which include cloud-based networks and edge networks, and which cater to a wide range of computation requirements. The coordinator cluster manages workloads by selecting the appropriate shard and worker nodes to execute tasks based on computed efficiency scores. Worker nodes may be incentivized with rewards such as cryptocurrency tokens for successful completion of tasks. The disclosed architecture also supports the pipelining of multiple tasks in a workload, where the output of one task is used as input for another task. In some respects, the computing architecture includes a blockchain-supported edge network, which enables verifying compute tasks and managing reward distribution for worker nodes using smart contracts.

The invention relates to a system and method for distributing decentralized computing resources. Computing resource metadata identifying a set of decentralized computing resources may be received. A request to use one or more computing resources may be received. Digital twins may be generated. The digital twinning may facilitate identification of a particular computing resource, and the digital twinning may represent potential interactions between a recipient entity and a set of provider entities. An interaction may be initiated between a recipient entity and a particular provider entity. The interaction may involve allocating a particular computing resource from a particular provider entity to a recipient entity in response to a request.

This invention provides encrypted digital assets for footwear items, a method for manufacturing / using such encrypted digital assets, and a decentralized computing system having blockchain control logic for mining, mixing, and exchanging blockchain-enabled digital shoes. One method of supplying digital assets includes receiving, from a first party to a second party, a transaction confirmation of a completed transaction indicating a footwear item or a digital file representing it, and transmitting encrypted blocks to a distributed blockchain ledger to record the transfer of the encrypted digital assets to the second party.

The present invention discloses an anonymous decentralized computing system and computing method based on group contracts, including group contracts. Group contracts use group signatures to protect the anonymity of individual group members while allowing traceability within the group. The group signature function is represented by a polynomial time algorithm. The group contract specifically includes: InitializeGroup, Register User , CountVote, Trace and Revoke; CountVote is an internal function, used only in tracking and revoking, used to check and enumerate non-duplicate votes; InitializeGroup, Register User , Trace and Revoke are public functions; InitializeGroup, Register User The codes for CountVote, Trace, and Revoke are encrypted and protected by TEE-DS. Workers securely interact with it, retrieve the encrypted codes, and decrypt them in the TEE. This invention does not rely on a trusted third party; while guaranteeing anonymity, it also ensures strong Byzantine fault tolerance and the ability to resist impersonators.

Disclosed is system for fulfilling service request using computing nodes. The system comprises a decentralized computing network, comprising computing nodes operating as autonomous agents, configured to implement software framework comprising: client-agent device configured to: receive service request and generate objective therefor, and send objective to agent-device configured to send queries to a machine learning model agent and / or obtain tasks, and list of autonomous agents associated with objective; and processing arrangement configured to: generate directed acyclic graph (DAG) having nodes representing first task and edges representing dependency of first task on second task(s) amongst tasks, and initialize Bloom filter of each node to track completion status of second task(s). Autonomous agent is configured to: identify second task(s), check completion status of second task(s), using Bloom filter of first task, upon completion of second task(s), execute first task, and update Bloom filter.

The disclosure relates to methods for predictive physics compression (PPC) for efficient simulations and calculation leveraging techniques described as fractal compression. Unlike traditional calculus-based methods relying on differential equations and related numerical methods, embodiments of the disclosure are directed towards representation and calculation of physical systems as hierarchical frames within a universal grid, where each frame encapsulates properties such as position and velocity. The simulations and calculations progress in discrete time steps recursively traversing the frame hierarchy. Each tick involves actions such as incorporating the previous frame relation's displacement, detecting collisions, applying stochastic movement and attractive force transforms, and limiting displacement based on defined constraints. Embodiments of the disclosure may be implemented on a single device or distributed across a network, with a coordination server distributing frames to client devices for parallel processing.

Decentralized computer systems and methods are disclosed for establishing and conducting payment processing with loyalty points using distributed ledger. One method includes: receiving a consumer identification, an issuer identification, and transaction information from a point of sale system when the loyalty card is presented for a transaction of a good or service at a merchant, identifying the network node containing distributed ledger associated with the issuer according to received issuer identification prompting, to participants of the shared ledger, to validate whether the consumer distributed ledger has required loyalty points for the transaction; if all of the participants of the shared ledger accepts the transaction, generating, a new immutable entry to the distributed ledger for the transaction with remaining loyalty points in the loyalty card after deducting the points required for the transaction; and sending a transaction approved message to the point of sale system at the merchant.

The invention relates to the field of decentration computing, and provides a fair and efficient multi-center federated consensus method based on a Shapley value, which cancels a central server parameter aggregation stage, enables nodes to be continuously trained on local data, takes each node as a central server and executes aggregation operation on each node. The objective of the invention is to solve the problem that decentration fairness is lack and global / local model performance is difficult to consider in the prior art. According to the main scheme, after each round of training is finished, the Shapley values of all nodes participating in consensus are calculated to quantify the marginal contribution of each node to a global model; cancelling a parameter aggregation mechanism of the central server, enabling each node to independently execute local parameter aggregation operation, and dynamically and randomly selecting a target node from all nodes based on a verifiable random function; and carrying out reweighting processing on the nodes participating in aggregation based on the Shapley value set, and carrying out fusion updating on the nodes and model parameters of the nodes to obtain locally optimized model parameters.

A client device operates by: receiving a start game request from a user corresponding to a game application having a user's NFT associated therewith, the NFT stored on a blockchain system via a plurality of different nodes of a decentralized computer system, the game application configured to start the game based on a verification of the user's ownership of the NFT; generating launch metadata; sending the launch metadata to a wallet associated with the user, the wallet containing a private key associated with the NFT; receiving, from the wallet, a launch response that is generated based on the private key; generating, based on the launch response, ownership check data; sending the ownership check data to the blockchain system that stores the NFT; receiving, from the blockchain system, an ownership validation response generated by the blockchain system based on geographically distinct blockchain calculations performed contemporaneously over the plurality of different nodes of the decentralized computer system, the ownership validation response indicating whether user's ownership of the NFT is verified; and enabling the start of the game associated with the game application when the ownership validation response indicates the user's ownership of the NFT is verified.

Decentralized computer systems and methods are disclosed for analyzing transaction attributes and dispute information using blockchain. One method includes: receiving a first transaction information of a first transaction, wherein the transaction information includes attributes of the first transaction; generating a blockchain entry that stores the received attributes in a shared ledger to participants of the blockchain; receiving an input of a selection of an attribute of the first transaction from a participant of the blockchain to analyze other transactions having the selected attribute; compiling a list of other transactions having the selected attribute; receiving transaction information for each transaction of the list of other transactions; and generating, on a user interface, an analytics report of the attributes of the other transactions.

A computer based system and method for providing decentralized training of a machine learning model, including: obtaining a mini-task of training the machine learning model wherein the mini-task comprises at least one mini-batch size; obtaining an estimation of at least one performance indicator in each trainer of a plurality of trainers that have available computing power, wherein each trainer comprises one or more processors; calculating a maximal micro-batch size for each processor of the plurality of trainers based on the at least one performance indicator of the respective trainer, and parameters of the mini-task; and assigning the mini-task to at least one designated trainer of the plurality of trainers based on the maximal micro-batch sizes of the one or more processors of the at least one designated trainer and the at least one mini-batch size.