International Business Machines Corporation

Lenovo (Singapore) Pte. Ltd.

Vinayaka D Pandit

Krishnasuri Narayanam

Pankaj Satyanarayan Dayama

Sreyash Kenkre

Sameep Mehta

Nitin Singh

Dhinakaran Vinayagamurthy

Venkatraman Ramakrishna

Balaji Viswanathan

Senthilnathan Natarajan

Rishi Saket

Akshat Verma

Karthik Visweswariah

Anindya Neogi

Gyana Ranjan Parija

Ermyas Abebe

Sandeep Nishad

Santosh Ravi Kiran Penubothula

Disha Shrivastava

Kameshwaran Sampath

Santosh R K Penubothula

Talha Ahmad Siddiqui

Harish Guruprasad Ramaswamy

Ramesh Baskaran

Vijay Ekambaram

Praveen Jayachandran

Diptikalyan Saha

Malolan Chetlur

Karthik Sankaranarayanan

Angelo De Caro

Akshar Kaul

Yogish Sabharwal

Indrajit Bhattacharya

Jaydeep Sen

Dushyant K Behl

Devasenapathi Periagraharam Seetharamakrishnan

Jayanta Basak

Om Dadaji Deshmukh

Samarth Bharadwaj

Dileban Karunamoorthy

Abhishek Singh

Venkatesan T Chakaravarthy

Raghavendra Uppinakuduru Udupa

Dayama Pankaj Satyanarayan

Soujanya Soni

Prabuchandran Krithivasan Jayachandran

Lohit K Namboodiri

Santosh Rk Penubothula

Varun R Embar

Johannes Adrianus Wilhelmus Röling

Joseph Nicolaas Gijsbertus Zeinstra

Yining Hu

A system and method for a multi-party computation (MPC) is provided. In implementations, a method includes identifying a blockchain network and a computing device to perform an MPC based on an index; generating an MPC request including a function to be performed by the blockchain network and the computing device, data required for the function, and a verification policy defining a verification protocol to be performed by the blockchain network and the computing device; sending the MPC request to the blockchain network and the computing device; and receiving responses from a representative computing node of the blockchain network and the computing device, wherein each of the responses includes: an output of an MPC protocol performed by the blockchain network and the computing device to jointly compute the function while keeping the inputs private from one another and private from the computing system; and a proof based on the verification policy.

Collaborative computation across blockchain networks

A computer-implemented method for automatically and digitally securing an asset is disclosed. The computer-implemented method includes hashing a secret preimage to produce a secret hash. The computer-implemented method further includes locking a digital collateral asset with the secret hash for a first predetermined period of time. The computer-implemented method further includes locking a digital loan asset with the secret hash for a second predetermined period of time, wherein the second predetermined period of time is less than the first predetermined period of time. The computer-implemented method further pledging a digital loan repayment up to the amount of time of the first predetermined period of time plus the second predetermined period of time. The computer-implemented method further includes responsive to receiving a pledge for the digital loan repayment, automatically unlocking the digital collateral asset for a borrower of the digital loan asset.

Method for digitally securing an asset

A group of transactions to be rolled back in a blockchain ledger can be identified. Whether the transactions in the group are eligible to be rolled back together can be determined. Other transactions can be recommended, and the other transactions may be added to the group to be rolled back together. A rollback transaction can be added to the blockchain ledger that rolls back effect of the transactions, where the rollback transaction has zero or more inputs and zero or more output, the rollback transaction having at least one selected from at least one input and at least one output. An unspent output of a transaction in the group is provided as an input of the rollback transaction. Input of a transaction in the group, that is not an output of another transaction in the group, is provided as an output of the rollback transaction.

Blockchain ledger rollback

An example operation may include one or more of connecting, by a lead peer, to a blockchain network of an organization configured to store assets, receiving, by the lead peer, from an ordering server, a block that contains a plurality of chaincode transactions belonging to plurality of peers of the blockchain network, applying, by the lead peer, a filter to the block, determining, by the lead peer, a recipient peer of the block in response to the application of the filter, receiving, by the lead peer, a validation result of the block from the recipient peer, and committing, by the lead peer, the block to the blockchain network.

Sparse peer with transient participation

An example operation may include one or more of connecting, by a sparse peer, to a blockchain network of a plurality of peers controlled by orderers, selecting, by the sparse peer, a subset of data to be replicated, specifying, by the sparse peer, data selection filters based on the selected subset of the data, providing, by the sparse peer, the selection filters to the plurality of the peers, receiving, by the sparse peer, blocks matching the selection filters over a gossip protocol from the plurality of the peers, constructing, by the sparse peer, a local ledger of the sparse peer from the received blocks, and bootstrapping the sparse peer based on the local ledger.

An example operation may include one or more of connecting, by a lead peer, to a blockchain network of an organization configured to store assets, receiving, by the lead peer, a block from an ordering server, that contains a plurality of chaincode transactions belonging to a plurality of peers of the blockchain, applying, by the lead peer, a filter to the block, determining, by the lead peer, a recipient peer of the block in response to the application of the filter, and receiving, by the lead peer, a notification of validation and commitment of the block to the blockchain by the recipient peer.

A processor may commit, anonymously, an identity and associated data of a resource producer on a blockchain by an independent gateway. The processor may generate a Merkle tree hash commitment to the blockchain of all resources handled by an aggregator. The processor may execute individual commitments of a resource to a user. The processor may provide a zero-knowledge-proof that proves that the commitment of the identity and associated data of the resource producer and the Merkle tree hash commitment match.

Trusted aggregation with data privacy based on zero-knowledge-proofs

One embodiment provides a method, including: receiving, at a service provider and from a user, a request for computation of a function, wherein the request includes a description of the function to be computed and requirements of the user; identifying, from data sources accessible to the service provider, a plurality of data sources to participate in the computation in view of the requirements and the description of the function, wherein each of the plurality of data sources includes data of a data owner and constraints on use of the data; identifying a multi-party computation protocol and parameters of the multi-party computation protocol for performing the computation; and running the computation of the function using the data of the plurality of data sources and in view of the requirements of the user and the constraints of the plurality of data sources.

Automation of multi-party computation as a service according to user regulations and requirements

An example operation may include one or more of identifying a data record to be included in a blockchain, creating a group of smart contracts to enable access to the data record to data consumers with access to the blockchain, determining the group of smart contracts have been registered by one or more blockchain peers, and storing the group of smart contracts on the blockchain.

Automated data projection for smart contract groups on a blockchain

Methods, systems, and computer program products for building an encrypted document store are provided herein. A computer-implemented method includes building an encrypted ordered key-value store; encrypting one or more sensitive keys to be stored in the ordered key-value store using an order preserving encryption scheme; storing a state of the order preserving encryption scheme in the ordered key-value store; and encrypting one or more values corresponding to the one or more sensitive keys using a semantically secure encryption scheme.

Building an encrypted document store

Methods, systems, and computer program products for verifying confidential machine learning models are provided herein. A computer-implemented method includes obtaining (i) a set of training data and (ii) a request, from a requestor, for a machine learning model, wherein the request is accompanied by at least a set of test data; obtaining a commitment from a provider in response to the request, the commitment comprising a special hash corresponding to parameters of a candidate machine learning model trained on the set of training data; revealing the set of test data to the requestor; obtaining, from the requestor, (i) a claim of performance of the candidate machine learning model for the test data and (ii) a proof of the performance of the candidate machine learning model; and verifying the claimed performance for the requestor based on (i) the special hash and (ii) the proof of the claimed performance.

Verifying confidential machine learning models

An example operation may include one or more of receiving a request to execute a blockchain transaction from a user device that is associated with a user account, determining whether to perform the blockchain transaction based on a dynamic participation policy of the user account, the dynamic participation policy being dynamically determined based on previous blockchain transactions of one or more of the user account and of other user accounts, and, in response to determining to perform the blockchain transaction, executing the blockchain transaction on behalf of the user account via a smart contract and storing information about the executed blockchain transaction.

Dynamic blockchain transactional policy management

Methods, systems and computer program products for automatic extraction and testing of constraints are provided herein. A computer-implemented method includes obtaining a first set of documents describing constraints and a second set of documents describing properties of entities, building a first dictionary of entity types and a second dictionary of relations among the entity types, extracting constraint triples representing the set of constraints from the first set of documents, and extracting fact triples from the second set of documents utilizing the first dictionary and the second dictionary. The method also includes receiving a query to evaluate whether at least one of the set of constraints is satisfied, determining whether the at least one constraint is satisfied by evaluating a constraint satisfaction formula utilizing the constraint triples and the fact triples, and providing a response to the query that indicates whether the at least one constraint is satisfied.

Automated constraint extraction and testing

An example operation may include one or more of receiving a request to merge a first blockchain network and a second blockchain network, the request comprising a script that specifies a network structure, synthesizing the script with configuration data of the first and second blockchain networks to generate a plurality of merge operations, and merging the first blockchain network with the second blockchain network based on the plurality of merge operations to create a merged blockchain network, wherein the merging comprises merging chaincodes and channels from the first and second blockchain networks into merged chaincodes and merged channels.

Automated merge of DLT networks

An example operation may include one or more of connecting, by a sparse peer, to a blockchain network of a plurality of peers controlled by orderers, configuring the sparse peer to operate with a partial state of a chain to reduce a bootstrap time of the sparse peer, specifying, by the sparse peer, data selection filters based on the partial state of the chain, receiving, by the sparse peer, blocks that match the selection filters from the plurality of the peers via a gossip, storing, by the sparse peer, the blocks in a ledger of the sparse peer, determining, by the sparse peer, if the ledger of the sparse peer is up-to-date based on the partial state of the chain, and in response to the determination that the ledger of the sparse peer is up-to-date, bootstrapping the sparse peer.

Growing community of inventors

New Delhi, India

Vinayaka D Pandit