International Business Machines Corporation

Servicenow, Inc.

Rensselaer Polytechnic Institute

Nitin Gaur

Kulvir Singh Bhogal

Christopher D Johnson

Petr Novotny

Douglas Berg

Dustin K Amrhein

Todd Eric Kaplinger

Konstantinos Christidis

Dulce Beatriz Ponceleon

Jeronimo Irazabal

Qi Zhang

Yacov Manevich

Anna D Derbakova

Timothy Olson

Praveen Jayachandran

Brian Keith Martin

Snehal S Antani

Srinivasan Muralidharan

Chris D Johnson

Ruslan Ardashev

Gregory J Boss

Aaron J Quirk

Andrew R Jones

Gregory Louis Truty

Venkatraman Ramakrishna

Lei Yu

Lisa Seacat DeLuca

Benjamin Michael Parees

Artem Barger

Sheehan Anderson

Gautham Pamu

Sloane Brakeville

Sarbajit Kumar Rakshit

Dana Lea Price

Donna N Dillenberger

Yada Zhu

Salman Abdul Baset

Palani Sakthi

Arthur L De Magalhaes

Abhishek Malvankar

Chander Govindarajan

Dushyant K Behl

William J O'Donnell

Wu Ning

Bishakh Chandra Ghosh

Daniel W Bingham

Malavan Balanavaneethan

Mayuran Satchithanantham

Hung Chung Kuo

Shikhar Kwatra

Yun Yu Wang

Scott N Gerard

Leigh Allen Williamson

Timothy S Olson

Garfield Winston Vaughn

Nathalie Baracaldo Angel

Lucian Popa

Sijia Liu

Kumar Bhaskaran

Luis Angel Bathen

Marshall Allen Lamb

Matthias Seul

Zhennan Wang

Sean K Dunne

Elezaveta Koumpan

Jinfang Chen

Wisam Mohammed

Peter Guglielmino

Koushik Kar

Benjamin S Vera-Tudela

Chung Yu Huang

Rolando Franco

Marcelo A Martins

Tetiana Iakovenko

Aparna Gupta

Sai Radhakrishna Manikant Sarma Palepu

Ruby Cheng

Wing Lun Chung

A graph neural network can be built and trained to predict a risk of an entity. A multi-relational graph network can include a first graph network and a second graph network. The first graph network can include a first set of nodes and a first set of edges connecting some of the nodes in the first set. The second graph network can include a second set of nodes and a second set of edges connecting some of the nodes in the second set. The first set of nodes and the second set of nodes can represent entities, the first set of edges can represent a first relationship between the entities and the second set of edges can represent a second relationship between the entities. A graph convolutional network (GCN) can be structured to incorporate the multi-relational graph network, and trained to predict a risk associated with a given entity.

Multi-relational graph convolutional network (GCN) in risk prediction

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

An example operation may include one or more of receiving, via a first blockchain peer, a request to verify an identity of a user, performing, via the first blockchain peer, an identity validation of the user via a know your customer (KYC) service provider of a blockchain network, determining whether a digital identity of the user has been previously stored by another blockchain peer on a blockchain of the blockchain network, in response to a determination that the digital identity of the user has not been stored on the blockchain, creating a non-fungible token (NFT) that identifies the user, and appending information about the identity validation of the user to a structure of the NFT and storing the NFT in a block of the blockchain.

Token-based identity validation via blockchain

A node in a blockchain network may agree, on an authority accept a compliance module from the authority, accept the compliance module. The node may also receive an operation, verify a compliance of the operation based on the compliance module, add the verified operation to a ledger on the blockchain network.

Compliance mechanisms in blockchain networks

An example operation may include one or more of encrypting a private key with an encryption key, generating a plurality of keys based on the encryption key and converting the plurality of keys into a plurality of key shares based on a secret input value, storing the encrypted private key on a blockchain, and distributing the plurality of key shares to a plurality of blockchain peers of the blockchain, where the distributing comprises transmitting a different key share from among the plurality of key shares to each blockchain peer among the plurality of blockchain peers.

Key reclamation in blockchain network via OPRF

An example operation may include one or more of receiving a request for storage at a blockchain network, attaching, via a blockchain node, a verifiable credential created by a self-sovereign identity (SSI) network to a blockchain transaction associated with the request, where the verifiable credential includes a claim of the blockchain node and a proof of the SSI network that created the verifiable credential, transmitting the blockchain transaction and the attached verifiable credential to one or more other blockchain nodes, and storing the blockchain transaction and the attached verifiable credential via a data block on the blockchain.

Blockchain network identity management using SSI

A node in a blockchain network may initiate a non-fungible token identity, establish a profile for the non-fungible token identity, receive reputation data related to the non-fungible token identity, determine, based on the reputation data, a reputation score, append the non-fungible token identity with the reputation score as a non-fungible token metadata tag, and upload the non-fungible token identity and the metadata tag to an ID repository.

Reputation profile propagation on blockchain networks

A plurality of ledger nodes within a blockchain infrastructure is continuously updated via a set of conduit nodes. The set of conduit nodes share a secure provision ledger. The secure provision ledger includes a set of updates for the blockchain infrastructure. Each conduit node within the set propagates the set of updates to a subset of the plurality of ledger nodes.

Ensuring secure provisioning of blockchain infrastructure

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

A node in a blockchain network may receive an event initiation for a first digital corollary of a first physical asset, receive conditional data related to the first digital corollary, detecting, by the node, a link to a second digital corollary, prompt a simulation of the second digital corollary, receive an outcome of the simulation, and determine an executable action for the second digital corollary.

Glue-contracts for digital corollaries in blockchain networks

A node in a blockchain network may generate a key for a first ring, wherein the key unlocks a first collection of data, defining a second ring including a second node of a second organization, derive a second key for a second collection of data, wherein the first key and the second key access the second collection of data, and distributing the second key to the second node.

Blockchain data segregation

A processor may segment a media key block into two or more subsets. Each of the two or more subsets may be respectively associated with a particular group of receivers, and each receiver of the particular group of receivers may be in a blockchain network. The processor may receive, from a first receiver, a request for permission to process the media key block. The processor may identify which of the two or more subsets that the first receiver is associated. The processor may provide a media key block value to the first receiver.

Decentralized, dynamic media key block for broadcast encryption

Described are techniques for generating and employing decentralized asset identifiers for cross-blockchain network asset transfers, the techniques including registering a decentralized asset identifier to an asset with a local asset identifier, where the decentralized asset identifier is immutable. The techniques further include endorsing a proposed transaction for transferring the asset from a first controller associated with a first blockchain network to a second controller associated with a second blockchain network, where the proposed transaction utilizes the decentralized asset identifier. The techniques further include exchanging, in a document associated with the decentralized asset identifier and retrieved from an identity network, the first controller for the second controller.

Decentralized asset identifiers for cross-blockchain networks

A node in a blockchain network may generate a secret information proof, generate a private/public key pair, encrypt the secret information proof with the private/public key pair, and submit the proof to a blockchain network.

Provable trade secrets on blockchain networks

An approach for blockchain transaction processing using generative cryptograms for bi-lateral and Multi-lateral transactions may be provided. The approach may include, the use of decentralization ledger storage, negating the need of repetitive storage while preserving the need for data redundancy. A generative cryptogram may provide verification of processed blocks and file handles or storage address schemes of the ledger storage. The approach creates a cryptogram which is added to transaction processing, post processing and commits to the ledger. In the approach, the cryptogram is the only version of storage pointer and is utilized in subsequent transaction processing without the need for extensive compute and latest cryptogram is all that would be needed for transaction processing.

Generative cryptogram for blockchain data management

Growing community of inventors

Round Rock, TX, United States of America

Nitin Gaur