International Business Machines Corporation

Artem Barger

Yacov Manevich

Hagar Meir

Yoav Tock

Alessandro Sorniotti

Jonathan Bnayahu

Angelo De Caro

Nitin Gaur

Petr Novotny

Elli Androulaki

Marko Vukolić

Senthilnathan Natarajan

Alexander Kofman

Eliezer Segev Wasserkrug

Gennady Laventman

Corville O Allen

Haggai Roitman

Pankaj Satyanarayan Dayama

Roy Levin

Yada Zhu

Alexey Roytman

Dany Moshkovich

Nir Rozenbaum

Bruno Wassermann

Roy Abitbol

Pratul Gupta

Wael Shama

A processor may store a write set of a transaction in a cache. The cache may include state information, and the cache may be uncommitted to a blockchain. The processor may identify a subsequent transaction. The subsequent transaction may read from the cache when a requested key is present in the cache. The processor may order the transaction and the subsequent transaction based on what is read from the cache. Ordering the transaction and the subsequent transaction may avoid an abort of the subsequent transaction.

Reducing transaction aborts in execute-order-validate blockchain models

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.

Future asset reclamation via blockchain

A processor may endorse the group of transactions received from one or more clients. The processor may provide a read-set and a write-set for each transaction of the group of transactions. The processor may order the group of transactions at an ordering service. The ordering service may have a group of orderers and may order the group of transactions. The order of the transactions may be based on the read-set and the write-set of each transaction. The processor may commit the group of transactions to the blockchain.

Transaction reordering in blockchain

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.

Trifocal key for controlling custodians of digital assets

In some embodiments, a processor may store a value and a hash of the value in a ledger associated with a blockchain network. The processor may construct a block hash from the hash of the value. The processor may validate a transaction by identifying that the hash of the value matches the hash of the value from a previous transaction. The processor may maintain integrity of the block hash. In some embodiments, a processor may provide a redact transaction for a blockchain network. The processor may initialize the redact transaction. The redact transaction may be associated with a validated transaction. The processor may identify a hash value indicated within the redact transaction and associated with the validated transaction. The processor may redact a value associated with the hash value.

Redactable blockchain

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.

Blockchain with random committee selection

An example operation may include one or more of a computer generating an output of a variable random function applied to a public key of a user of a blockchain and a hash of the blockchain block, the computer determining a function f that gives a number x in [0,1] to each of a plurality of transactions on the block based on the output, and the computer ordering the transactions, using the function f, within the block.

Fair transaction ordering in blockchains

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.

Verifiable privacy preserving computation

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.

Malicious peer identification

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.

Multi-party transactions in a computerized database

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.

Malicious peer identification for database block sequence

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.

Mitigating risk in business networks in a privacy preserving manner

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.

Limiting data availability on distributed ledger

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.

Preservation of channel metadata

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. In an execute-order model of permissioned blockchains, one-time use keys may be registered with the certificate authority of the blockchain, and used to co-sign transactions that might appear suspicious, preventing false-positive identification of suspicious-looking transactions by the classifier.

Growing community of inventors

Haifa, Israel

Artem Barger