Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: An example operation may include one or more of receiving, by a document validation node, documents from a plurality of document owner nodes over a blockchain network, generating, by the document validation node, commitments for the documents on the blockchain network, deriving, by the document validation node, proofs to verify predicates of the documents, and generating, by the document validation node, a document relationship graph (DRG) based on the commitments and the predicates.

Abstract: An example operation may include one or more of storing a tree structure via a blockchain storage, the tree structure comprising anonymous behavior data of a plurality of blockchain participants stored in a plurality of nodes in a hierarchical structure, receiving a request to add new anonymous behavior data to the tree structure, the request comprising a zero-knowledge proof generated by a blockchain participant, identifying an active leaf on the tree structure which stores previously recorded anonymous behavior data of the blockchain participant associated with the request based on the zero-knowledge proof, generating a new active leaf for the blockchain participant based on the new anonymous behavior data and the previously recorded anonymous behavior, and storing the new active leaf as a leaf node on the tree structure in the blockchain storage.

Abstract: One embodiment provides a method, including: obtaining historical information for equipment having at least one control, wherein the historical information indicates a setting for the at least one control during operation of the equipment and identifies operating performance of the equipment corresponding to the indicated setting; receiving a goal for the equipment, wherein the goal is related to a desired operating performance of the equipment; identifying, a plurality of sets of contiguous good reference segments, wherein a contiguous set of good reference segments comprises a plurality of operating time segments where the desired operating performance goal was achieved for a predetermined of time; identifying, a subset of sets comprising reference segments that are achievable from a current operating state of the equipment; selecting, a reference segment that is attainable based upon exogenous factors related to an operating environment of the equipment; and providing a recommendation to an operator of the

Abstract: 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.

Abstract: One embodiment provides a method, including: receiving input identifying a goal of a student, wherein the goal indicates (i) a target concept to be learned by the student and (ii) a desired expertise corresponding to the target concept; receiving input indicating constraints comprising (i) a time budget and (ii) an effort budget; and generating a syllabus for the student to reach the goal, wherein the syllabus comprises a sequence of sub-concepts to be learned for reaching the goal, by: producing a plurality of alternative sequences of sub-concepts for reaching the identified goal, each sequence of sub-concepts having both a determined, corresponding (i) effort cost and (ii) time cost, wherein determining the corresponding effort cost and time cost comprises identifying relationships between sub-concepts; and determining, from the plurality of alternative sequences, a particular one of the sequences that reaches the target concept at the desired expertise and fulfills the indicated constraints.

Abstract: An example operation may include one or more of receiving a content request to add a content to a blockchain storage structure implemented on a blockchain database, temporarily storing the content of the content request in a queue implemented via the blockchain database based on when the request is received, receiving a request to flush the queue which is invoked by chaincode, and in response to the flush request, removing the content from the queue and adding the content to the blockchain storage structure.

Abstract: Methods and systems for managing energy sources and energy consumers in an integrated system are provided. Certain subject matter presented herein relates to automatically scheduling jobs and their sub-tasks to maximize profit by comparing power source configurations and determining the best job schedule for the power source configurations. This system broadly involves two sub-problems: determining the best power source configuration and determining the best job schedule for the given power source configuration.

Abstract: 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.