International Business Machines Corporation

Qpq Ltd

Gokhan Sagirlar

Emanuele Ragnoli

Stefano Braghin

Killian Levacher

Martin Wistuba

John Sheehan

Mansura Habiba

Greg Chew

The present disclosure provides a self-adaptive sharding engine, SASE, which is configured to dynamically adapt the configuration of connected DL T platforms to ensure that their operational performance is maintained within target levels. The SASE is provided with a decision and optimisation, DO, module that is configured to receive and process a set of configuration parameters obtained from a sensor module to determine a set of corresponding performance metrics indicative of the operational performance of the target DLT platform. The DO module is configured to compare the values of the performance metrics with corresponding target values. Accordingly, when at least one of the performance metrics is within a value range from at least one corresponding target value, the DO module is configured to adjust an aspect of the DLT configuration of the DLT platform to optimise its operational performance.

System and method for self-adaptive and autonomous sharding of distributed ledger technology platforms

Embodiments are disclosed for a method. The method includes validating training data that is provided for training a machine learning model using ordinary differential equations. The method further includes generating pre-processed training data from the validated training data by generating encrypted training data from the validated training data using homomorphic encryption and generating random noise based on the validated training data. The method also includes training the machine learning model adversarially with the pre-processed training data.

Security as a service for machine learning

Embodiments for ensemble training in a distributed marketplace in a computing environment. One or more ensemble machine learning models may be provided from a plurality of machine learning models competing within the distributed marketplace that achieve a performance on ensemble training data equal to or greater than a selected performance threshold, wherein the distributed marketplace is a blockchain.

Ensemble training in a distributed marketplace

A system and method for implementing a marketplace for risk assessed smart contracts issuers and execution providers in blockchains in a computer environment are presented. A smart contract, issued by one or more blockchain nodes to a secondary blockchain and accepted by a primary blockchain, may be executed following a risk assessment to recursive call attack vulnerabilities of the smart contract according to a reputation of both the one or more blockchain nodes and the one or more execution nodes.

Implementing a marketplace for risk assessed smart contracts issuers and execution providers in a blockchain

A system and method for asset leak risk assessment in blockchains are presented. A risk assessment of recursive call attack vulnerabilities may be cognitively determined according to risk vulnerability measurements generated from a computer program source code, a list of external call functions, a risk assessment function, a list of assets, a parser, or a combination thereof.

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Dublin, Ireland

Gokhan Sagirlar