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.

Abstract: A system may enact a process including initiating an operation to transfer a secret, establishing n groups of nodes in a blockchain network, dividing a secret into n secret parts, creating n group keys, encrypting each secret part of the secret with a corresponding group key, and recording encrypted secret parts on the blockchain networks.

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 quality determination method, system, and computer program product that includes performing a dimensionality reduction on a high-dimensional dataset to form a dimensional-reduced dataset and determining, using a machine learning tool executed on a computing device, a quality of the dimensional-reduced dataset via a review of an extracted feature extracted from the dimensional-reduced dataset.

Abstract: The processor may collect, from a first peer, a first data set associated with a channel. The channel may include one or more assets associated with a first peer. The channel may link the first peer to a second peer. The processor may analyze the first data set to determine a first status for the one or more assets. The processor may provide the first status of the one or more assets in a viewable channel to the blockchain network.

Abstract: An example operation may include one or more of retrieving decentralized identifiers (DIDs) of a plurality of blockchain peers included within a blockchain network, generating a blockchain declarative descriptor (BDD) which uniquely identifies the blockchain network, where the BDD comprises a machine-readable data file with a first field includes the retrieved DIDs of the blockchain network, a second field including signature data of the plurality of blockchain peers, and a third field including metadata, and transmitting the generated BDD to a blockchain network registry.

Abstract: An example operation may include one or more of receiving, by a risk score module, a blockchain transaction proposal, obtaining transaction proposal data, obtaining external data, computing a risk score from the transaction proposal data and the external data, comparing the risk score to a risk score threshold, providing a commitment decision, based on the comparison, and one of committing or rejecting an endorsed transaction corresponding to the transaction proposal.

Abstract: A client computer may split a process into sub-processes, send each sub-processes to a different group of peers in a blockchain network, wherein each group has at least one peer from each essential organization in the blockchain network, receive processed sub-transactions from the peers in the blockchain network, validate each sub-transaction, and validate the transaction based on the validation of all sub-transactions, wherein all sub-transaction must be valid for the transaction to be valid.

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: In an approach for creating self-sovereign credit token issuance to facilitate account to account lending and creation of a digital credit market, a processor identifies a digital interaction within an edge of connected accounts, wherein the digital interaction includes an account-to-account transaction having a total economic impact within the edge of connected accounts. A processor evaluates, based on the digital interaction, potential future earnings of a fungible token and a non-fungible token. A processor provides a criterion to generate a self-sovereign, self-issued credit token that is embedded with a credit worthiness of the self-sovereign, self-issued credit token, based on the total economic impact and the potential future earnings.

Abstract: A processor may record one or more transaction proposals regardless of validity of the one or more transaction proposals. The processor may separate a transaction validation into a series of steps. The processor may validate, asynchronously, one or more transactions respectively associated with each of the one or more transaction proposals based on the transaction validation.