Abstract: Disclosed herein are methods and systems for executing verifiable computation modules to process private data at private data owner platform, comprising obtaining a computation module having a unique identifier recorded in a distributed ledger controlled by a plurality of computing nodes, generating a key pair comprising a signing key and a verification key derived from the signing key, recording, in the distributed ledger, an execution record associating an execution instance of the computation module with the verification key, initiating the execution instance of the computation module to process a private dataset incorporated with the signing key. outputting a computation outcome, computing an execution result signature for the execution instance based on the unique identifier and the private data and the signing key, and recording the execution result signature in the distributed ledger to enable verification of the execution instance.

Abstract: Maintaining, for each target key in a key-value computer database, a signature policy; creating a transaction candidate in the database based on a transaction context submitted to the database by a first user, the transaction candidate comprising: a key, a value comprising a transaction content and a signature of the first user, and a signors list of additional one or more users who are required to co-sign the transaction candidate before the transaction candidate is committed in the database as a transaction, wherein the signors list is automatically computed based on the signature policies of the one or more target keys; collecting a signature for the transaction candidate from at least one of the additional one or more users, according to the signors list; verifying that all signatures required by the signors list have been collected; and in response to the verification, committing the transaction content of the transaction candidate.

Abstract: An example operation may include one or more of receiving, by each of one or more peripheral peers of a blockchain network, a new block from an orderer peer, calculating a hash of the new block, determining the calculated hash is different than hashes from a majority of peripheral peers, determining that one or more blocks that correspond to the different hashes from the majority of peripheral peers are different from the new block, and in response ceasing committing blocks to the blockchain network.

Abstract: An example operation may include one or more of receiving, by each of one or more peripheral peers of a blockchain network, a sequence of new blocks from an orderer peer, calculating hashes for the sequence of new blocks, adding the hashes to a merkle tree, determining the merkle tree is different than merkle trees from a majority of peripheral peers, determining that one or more blocks that correspond to the different merkle trees from the majority of peripheral peers are different from the sequence of new blocks, and in response ceasing committing blocks to the blockchain network.

Abstract: Aspects of the present invention disclose a method for calculating a risk resulting from a network of networks that includes unknown relationships in a privacy preserving manner. The method includes one or more processors determining a set of conditions corresponding to a user of a network. The method further includes transmitting a compliance request corresponding to the set of conditions to one or more members of the network utilizing a privacy preserving algorithm. The method further includes determining a respective risk factor of one or more members of the network, wherein the respective risk factor corresponds to a response of each of the one or more members to the compliance request. The method further includes determining an overall risk of the network based at least in part on the risk factors of the one or more members.

Abstract: Systems, methods, and computer programming products leveraging the use of machine learning, cryptographic keys and blockchain technology for validating blockchain transactions. The disclosed systems, methods and products improve detection of malicious cyberattacks and fraud, while reducing occurrences of falsely invalidated transactions and improving overall blockchain security in both permissioned and permissionless blockchain networks. Classifiers are trained using machine learning and other classification techniques by building a transaction history to learn how to identify suspicious transactions on the blockchain. In permissionless and order-execute models of permissioned blockchains, cryptographic keys are publicly registered to guardians residing out of band, who may co-sign requests and override or resubmit transactions marked as suspicious by the classifiers.

Abstract: An example operation includes one or more of generating, by a channel peer, a random value to be associated with a channel name, mapping, by the channel peer, the random value to a block range, receiving, by the channel peer, a channel-MAC from a second peer, and validating the channel-MAC based on the channel name and the random value.

Abstract: An example operation may include one or more of encrypting content via an encryption key to generate encrypted content, storing the encrypted content via a distributed ledger, splitting the encrypted encryption key into a set of key shares via a threshold secret sharing scheme, and distributing the set of key shares among a plurality of nodes of a distributed vault, where each key share is distributed with an expiry value that identifies when the respective key share is to be deleted by a node.

Abstract: An example operation includes one or more of receiving view change messages which request a view change from a previous primary peer of a blockchain to the primary peer, identifying that a change to a state of the blockchain is in process with the previous primary peer based on metadata of the received view change messages, verifying that the change to the state of the blockchain corresponds to a latest change to the blockchain based on a received view data message, and transmitting a new view message to following peers which includes the in-process change to the state of the blockchain.

Abstract: Systems, methods, and computer programming products leveraging the use of machine learning, cryptographic keys and blockchain technology for validating blockchain transactions. The disclosed systems, methods and products improve detection of malicious cyberattacks and fraud, while reducing occurrences of falsely invalidated transactions and improving overall blockchain security in both permissioned and permissionless blockchain networks. Classifiers are trained using machine learning and other classification techniques by building a transaction history to learn how to identify suspicious transactions on the blockchain. In permissionless and order-execute models of permissioned blockchains, cryptographic keys are publicly registered to guardians residing out of band, who may co-sign requests and override or resubmit transactions marked as suspicious by the classifiers.

Abstract: In an approach for computing trade-offs between privacy and accuracy of data analysis on building a learning model, a processor receives a dataset for training a model. The dataset includes one or more pre-identified sensitive data fields. The processor determines a weight of each sensitive data field for the model. The processor evaluates resource cost of applying a privacy preservation technique to the one or more pre-identified sensitive data fields. The processor identifies correlation among the sensitive data fields. The processor presents a comparison of options for training the model, in terms of tradeoffs of accuracy for training the model and the resource cost of the privacy preservation technique.

Abstract: An example operation includes one or more of receiving view change messages which request a view change from a previous primary peer of a blockchain to the primary peer, identifying that a change to a state of the blockchain is in process with the previous primary peer based on metadata of the received view change messages, verifying that the change to the state of the blockchain corresponds to a latest change to the blockchain based on a received view data message, and transmitting a new view message to following peers which includes the in-process change to the state of the blockchain.

Abstract: A node in a blockchain network generates a non-fungible token (NFT) and obtain docket information for the NFT comprising transfer conditions for the NFT. The node also generates a docket credential with embedded transfer terms for the NFT from the docket information as part of a signature scheme for the NFT and signs an NFT block on a blockchain network with the docket credential.

Abstract: Maintaining, for each target key in a key-value computer database, a signature policy; creating a transaction candidate in the database based on a transaction context submitted to the database by a first user, the transaction candidate comprising: a key, a value comprising a transaction content and a signature of the first user, and a signors list of additional one or more users who are required to co-sign the transaction candidate before the transaction candidate is committed in the database as a transaction, wherein the signors list is automatically computed based on the signature policies of the one or more target keys; collecting a signature for the transaction candidate from at least one of the additional one or more users, according to the signors list; verifying that all signatures required by the signors list have been collected; and in response to the verification, committing the transaction content of the transaction candidate.

Abstract: A processor may assign an indicator to each participant in a blockchain network. The indicator may respectively indicate a role of each participant. The processor may transform the indicator of each participant into respective scores. The respective scores may be directly associated with the role of each participant. The processor may aggregate the respective scores into an endorsement score. The processor may determine if the endorsement score exceeds an endorsement threshold.

Abstract: A computer-implemented system and method process a private transaction for dividing a blockchain transaction into a plurality of requests. Each of the requests have transaction data and a same transaction identifier. Each request is sent to a respective participating shard of a set of participating shards in the blockchain transaction of the blockchain. A response to the request is received from each participating shard, and each of the responses is sent to at least the participating shards except the shard the response was received from. In an atomic commit operation, a prepare transaction is created comprising information from the responses and the transaction identifier. The prepare transaction is submitted to the blockchain, and validity answers are received from the shards.

Abstract: An example operation may include one or more of transmitting a user identifier to a plurality of blockchain peers of a blockchain network, receiving a plurality of public keys from the plurality of blockchain peers, receiving a plurality of ciphertexts from the plurality of blockchain peers generated based on the user identifier and a corresponding plurality of private keys, generating a combined ciphertext that includes the plurality of ciphertexts and generating a temporary blockchain address of the blockchain network based on the plurality of public keys, and storing the combined ciphertext and a transfer of an asset via the temporary blockchain address of the blockchain network.

Abstract: An example operation may include one or more of receiving, via a custodial service, a request to transact with a digital asset owned by a user and temporarily in custody of the custodial service, generating a blockchain transaction comprising an identifier of the digital asset on a blockchain ledger, an identifier of the custodial service, and an identifier of a recipient of the digital asset, signing the blockchain transaction with a key from a trifocal key which proves that the custodial service is authorized to transact with the digital asset on behalf of the user, and storing the signed blockchain transaction on a blockchain ledger.

Abstract: Disclosed herein are methods and systems for executing verifiable computation modules to process private data at private data owner platform, comprising obtaining a computation module having a unique identifier recorded in a distributed ledger controlled by a plurality of computing nodes, generating a key pair comprising a signing key and a verification key derived from the signing key, recording, in the distributed ledger, an execution record associating an execution instance of the computation module with the verification key, initiating the execution instance of the computation module to process a private dataset incorporated with the signing key. outputting a computation outcome, computing an execution result signature for the execution instance based on the unique identifier and the private data and the signing key, and recording the execution result signature in the distributed ledger to enable verification of the execution instance.

Abstract: An example operation may include one or more of storing blockchain blocks committed to a blockchain based on a protocol executed by a current consensus committee of a blockchain network, receiving random values from the blockchain blocks which are created by nodes of the current consensus committee, randomly determining nodes of a next consensus committee of the blockchain network with respect to the current consensus committee based on the random values created by the nodes of the current consensus committee, and storing a new block to the blockchain based on a protocol based executed by the nodes of the next consensus committee.