Abstract: An example operation includes one or more of detecting, by a validation service node, an access of a block on a blockchain ledger; accessing, by the validation service node, a block validation stamp comprising meta-data signed by a trusted stamping authority (TSA) on the blockchain ledger; and validating the block based on the meta-data.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Mechanisms for performing intelligent federated machine learning (ML) model updates are provided. A plurality of ML model updates, and a plurality of dataset sketch commitment data structures (sketches), are received from a plurality of participant computing systems. Each sketch provides statistical characteristics of a corresponding local dataset used by a corresponding participant to train a local ML model. A potentially malicious participant identification operation is performed based on an analysis of the plurality of sketches to identify one or more potentially malicious participants based on differences in sketches. ML model updates received from participant computing systems identified as potentially malicious participants are discarded to thereby generate a modified set of updates. The federated ML computer model is updated based on the modified set of updates.

Abstract: Embodiments of the present invention provide methods, computer program products, and systems. Embodiments of the present invention can receive, by a computing device, a request to access a datapoint of a machine learning dataset contained in a database. Embodiments of the present invention can access, by the computing device, a virtual data frame that includes a schema which represents a structure of the machine learning dataset in the database. Embodiments of the present invention can retrieve, by the computing device, the datapoint of the machine learning utilizing the virtual data frame and return, by the computing device, the retrieved datapoint in response to the request.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method, system, and computer program product are disclosed for securely orchestrating containers in a container orchestration environment. The containers comprise confidential containers running in a trusted execution environment (TEE) and standard containers running in the container orchestration environment. The containers are securely orchestrated without modifying the containers, container runtimes, and platforms, protecting sensitive data and code of the containers by restricting access to containers.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A computer-implemented method and a computer system are disclosed for generating and executing a software appliance comprising the steps of receiving a work request comprising metadata which indicates processing conditions for a computer processing task to be performed; determining a determined processing environment, depending on the metadata included in the work request; selecting one or more software container templates, depending on the metadata included in the work request; generating the software appliance for the determined processing environment, using the one or more selected software container templates; and deploying the software appliance for executing the software appliance on a processing computer system of the determined processing environment.

Abstract: An example operation may include one or more of establishing a communication channel between a sending system and a receiving system, executing an oblivious transfer protocol between the sending system and the receiving system via the established communication channel, wherein the oblivious transfer protocol provides the receiving system with a functional encryption key based on a data vector of the receiving system without the sending system learning the data vector, committing to the functional encryption key using a cryptographic commitment and signing the functional encryption key commitment with a digital key of the receiving system, and storing the signed functional encryption key commitment to a blockchain.

Abstract: Providing a representative dataset from an initial dataset by accessing a dataset associated with a machine learning model, receiving input parameters associated with the representative dataset selection, the input parameters including an evaluation metric, determining a density of a plurality of datapoints associated with the dataset, training a first iteration of a machine learning model using a first data point selected according to the density, determining a first value of the evaluation metric for the first iteration of the machine learning model, generating a representative subset based on the first value of the evaluation metric value, and providing the representative dataset and a final machine learning model trained using the representative dataset.

Abstract: 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.

Abstract: A processor may incorporate one or more keys in a media. The one or more keys may each be associated with a specific instance and the one or more keys may be included in a blockchain. The processor may identify that a first specific instance has been encountered. The processor may provide a first key associated with the first specific instance. The processor may determine to allow access to content of the media.

Abstract: A method and apparatus for loading balancing among blockchain peers by using a probing transaction from a client node to identify a latency of each blockchain peer. The client node can then select which blockchain peers to use based on the identified latencies.