International Business Machines Corporation

Globalfoundries Inc.

Kapil Kumar Singh

Ashish Kundu

Suresh N Chari

Lawrence H Koved

Pau-Chen Cheng

Arjun Natarajan

Dong Su

Joshua F Payne

Nataraj Nagaratnam

Dimitrios Pendarakis

Mihai Christodorescu

Hasini Gunasinghe

Sridhar R Muppidi

Sharon Mary Trewin

Calvin Bruce Swart

Heqing Huang

Marc Philippe Stoecklin

Douglas Lee Schales

Reiner Sailer

Josyula R Rao

Xin Hu

Hugo M Krawczyk

Benjamin E Andow

An example operation may include one or more of connect to a blockchain network of an ecosystem comprised of a plurality of consumer nodes, generate a universal unique identifier (UUID) associated with a user data attribute, execute a query request for the user data attribute associated with the UUID, derive a disclosure level from the query request, execute a smart contract to commit to a blockchain ledger, a monetary value associated with the user data attribute, negotiate a transaction with the consumer node to access the user data attribute and record an agreement result onto the a blockchain ledger.

Identity ecosystem

A deep-learning based method evaluates similarities of entities in decentralized identity graphs. One or more processors represent a first identity profile as a first identity graph and a second identity profile as a second identity graph. The processor(s) compare the first identity graph to the second identity graph, which are decentralized identity graphs from different identity networks, in order to determine a similarity score between the first identity profile and the second identity profile. The processor(s) then implement a security action based on the similarity score.

Deep learning-based similarity evaluation in decentralized identity graphs

A method provides a security action based on identity profile scores. One or more processors represent an identity profile as a knowledge graph. The processor(s) associate a set of changes of the identity profile across a plurality of identity networks with a fraud score. The processor(s) then implement a security action based on the fraud score.

Deep learning-based identity fraud detection

An approach is provided that identifies Internet of Things (IoT) network anomalies. The approach receives IoT endpoint device data at an attestation entity included in the network. The data is logged to a secured ledger and analyzed. Conditions pertaining to the IoT endpoint devices are analyzed with the analysis being based on a set of network policy data. Based on the analysis, the approach detects network anomalies that correspond to the IoT endpoint devices. These network anomalies and their corresponding IoT endpoint devices are then reported.

Identifying internet of things network anomalies using group attestation

A method provides a network-agnostic identity broker for retrieving identity records across heterogeneous identity networks. An identity broker receives a client request from a client to retrieve and evaluate user identity information for confirming an identity of a particular entity. The identity broker utilizes a group membership of the client to select a set of policies for handling the client request, and selects an identity network from multiple heterogeneous identity networks as a selected identity network to which the client request is to be sent. The identity broker sends the client request to the selected identity network, and then receives a response from the selected identity network. The identity broker evaluates the response according to the set of policies, such that the evaluated response conforms with the set of policies, and transmits the evaluated response to the client.

Adaptive identity broker for governance of decentralized identities across multiple heterogeneous identity networks

An example operation may include one or more of receiving, by a blockchain node or peer of a blockchain network, attribute data for a user profile, creating blockchain transactions to store attribute hashes and metadata to a shared ledger, receiving a user profile query from an identity consumer, creating blockchain transactions to retrieve attribute hashes and metadata corresponding to the query, reconstructing the user profile from the metadata, responding to the query by providing attribute data to the identity consumer, and creating and storing hashes of the attribute data and metadata to the shared ledger.

Decentralized database identity management system

A method routes an identity query to a particular identity network. An identity broker determines that a candidate identity network is associated with a confidence score that satisfies predetermined criteria. Responsive to determining that the candidate identity network is associated with the confidence score that satisfies the predetermined criteria, the identity broker onboards the candidate identity network into a set of identity networks services, and then routes an identity query for an identity to the candidate identity network that satisfies the predetermined criteria.

Identity network onboarding based on confidence scores

An approach is provided that enhances computer system security. In the approach, a set of users is authorized to be notified when any of a selected set of activities occurs on the user's account. When the system detects that one of the activities has occurred on the account, a notification is sent to the set of authorized users. The set of users may individually send a responsive security response to protect the user's account. Responsive to receiving the security response from one of the set of users, a security action is performed that is anticipated to protect the user's account.

Leveraging social relationships to enhance computer security

A blockchain hierarchy comprises an arrangement of blockchain channels organized such that a blockchain channel at a lower level feeds data to another blockchain channel, e.g., a relatively higher level. At least one blockchain channel comprises a private autonomous subset of peers in the set of peers that comprise the blockchain network. Within that particular subset, one of the peers is elected as a leader entity, which has the capability of also joining another private or public blockchain channel, e.g., one at a higher level in the hierarchy. The leader entity includes a capability to enforce a data protection policy within the blockchain channel that it leads. To this end, the leader filters or declassifies data based on some task-specific (or blockchain channel-specific) data protection policy, and it then makes that data available to one or more other blockchain channels in the hierarchy (directly or indirectly).

Protecting sensitive data in a distributed ledger system using a blockchain channel hierarchy

A blockchain hierarchy comprises an arrangement of blockchains organized in a tree-like manner such that a blockchain at a lower level feeds data to a blockchain at a relatively higher level. At least one blockchain comprises a private autonomous group of peers that are involved in a particular task. Within that particular group of peers, preferably one of the peers is elected as a leader entity, which has the capability of also joining another private or public blockchain, e.g., one at a higher level in the hierarchy. The leader entity includes a capability to enforce a data protection policy within the blockchain that it leads. To this end, the leader filters or declassifies data based on some task-specific (or blockchain-specific) data protection policy, and it then makes that data available to one or more other blockchains in the hierarchy (directly or indirectly).

Protecting sensitive data in a distributed ledger system using blockchain hierarchies

A processor-implemented method facilitates identity exchange in a decentralized setting. A first system performs a pseudonymous handshake with a second system that has created an identity asset that identifies an entity. The second system has transmitted the identity asset to a third system, which is a set of peer computers that support a blockchain that securely maintains a ledger of the identity asset. The first system transmits a set of pseudonyms to the third system, where the set of pseudonyms comprises a first pseudonym that identifies the first system, a second pseudonym that identifies a user of the second system, and a third pseudonym that identifies the third system. The first system receives the identity asset from the third system, which securely ensures a validity of the identity asset as identified by the first pseudonym, the second pseudonym, and the third pseudonym.

Privacy-preserving identity asset exchange

A processor-implemented method improves security in a blockchain network of devices by protecting security, privacy and ownership assurance of identity assets, where the blockchain network of devices supports a blockchain. An identity asset provider device establishes co-ownership of an identity asset for an identity asset provider and an entity. The identity asset provider device directs a first member of the blockchain network of devices to associate identities of the identity asset provider and the entity based on their co-ownership of the identity asset by using commitments between the identity asset provider and the entity and based on collaborative proof of ownership of the identity asset using zero knowledge proofs in the blockchain network of devices.

Protection of confidentiality, privacy and ownership assurance in a blockchain based decentralized identity management system

A processor-implemented method improves security in a blockchain network of devices, which supports a blockchain, by protecting security, privacy, financial fairness, and secure transfer of identity assets. An identity asset provider device creates an identity asset related to an entity. The identity asset provider also creates a provider key, which is composed of multiple bits, and which is needed to decrypt an encrypted version of the identity asset. The identity asset provider device transmits the provider key bit-by-bit to an identity asset consumer device. A price for the provider key depends on how many bits have been transmitted to the identity asset consumer device.

Protection of confidentiality, privacy and financial fairness in a blockchain based decentralized identity management system

One or more processors mark a set of data fields associated with a first trigger in a first trigger-action pair with a taint, where a trigger event triggers an action event in a trigger-action pair. One or more processors mark a first action associated with the first trigger-action pair with the taint, and detect a second trigger associated with a second trigger-action pair. One or more processors then propagate the taint from the first trigger-action pair to the second trigger, and prevent a second action associated with the second trigger-action pair in response to detecting the taint in the second trigger.

Enforcing access control in trigger-action programming using taint analysis

An approach is provided that provides data protection in a mobile device. The approach monitors a set of sensor data at the mobile device to determine a current context of the mobile device. Sensor data can include data pertaining to the external environment as well as to the user's current interaction with the device. In response to determining a negative current context of the mobile device, the approach deletes an encryption/decryption key from the mobile device rendering the encrypted data on the device inaccessible to malevolent users and data thieves.

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Cary, NC, United States of America

Kapil Kumar Singh