Accenture Global Solutions Limited

Stevens Institute of Technology

Andrea Maria Milazzo

Kuntal Dey

Vikrant Shyamkant Kaulgud

Giuseppe Giordano

Luca Schiatti

Emmanuel Viale

David Baurle Treat

Giuseppe Ateniese

Abdoulaye Faye

Haris Pasic

Tracy Ann Kuhrt

Sankar Narayan Das

Dominic Paolino

Angelo Massimo Perillo

Danilo Francati

Guiseppe Giordano

This disclosure is directed to a computer system and method to assist in identifying data feature intersection or overlap between private datasets without revealing any specific data items or data features in the datasets. Various technical components including natural language processing, lexical optimization, and encryption and key management technologies such as homomorphic encryption and secret sharing and coding, are integrated into the disclosed system and method to achieve the data feature intersection identification. Such a system and method may be employed in circumstances where data feature intersection is important for collaborative efforts between entities.

System for restrictive discovery of private data feature intersection

A system may provide blockchain-based storage node control for unified proof-of-storage interaction for off-chain data chunk storage. The system may include verification circuitry that may provide a challenge to a storage node to audit the storage status of a data chunk. The verification circuitry may obtain identities for the storage node and data chunk based on blockchain records. The verification circuitry may obtain a challenge answer from the storage node in response. The verification circuitry may analyze the challenge answer in view of a challenge key to confirm whether the storage node has possession of the data chunk. When the storage node has possession of the data chunk chain circuitry may add a verification record to the blockchain.

Cryptologic blockchain-based off-chain storage verification

A system includes a display control prime located within a central-zone in communication with a provisioned agent operating on an edge-zone device. The display control prime may implement prime-blind open-loop pixel-state control on the edge-zone device by executing pixel-state commands via the provisioned agent. The provisioned agent, operating within the edge-zone, may have access to un-sanitized data, which may be unavailable to the display control prime in the central-zone. The display control prime may provide conditional pixel-state commands via a pixel-state control parameter matrix. The provisioned agent may identify an operative pixel-state command from among the conditional pixel-state commands based on the un-sanitized data to which the display control prime is blind.

Privacy-secure edge-zone digital identity computing for open-loop pixel-state control

A commodity-measuring device for autonomously selecting a commodity supplier among a plurality of commodity suppliers being specified within a blockchain distributed database is provided. A microcontroller unit is configured to provide commodity consumption data to the transaction manager. A storage is configured to store a private key, and a communication module is configured to retrieve a commodity supplier file stored in the blockchain distributed database, and the commodity supplier file includes parameters of a commodity supplier and a program. A transaction manager is configured to determine autonomously, based on the commodity consumption data and parameters in the commodity supplier file, whether to select the commodity supplier; and if the determination is positive, the transaction manager is configured to, using the private key, send a transaction to the blockchain distributed database calling a function in the program to associate the commodity supplier file with the identifier.

Device, method and system for autonomous selection of a commodity supplier through a blockchain distributed database

This document generally describes techniques for using a distributed ledger to implement a framework for the validation and distribution of virus signatures, which may be used by antivirus engines on computing devices to detect and remove malware. Some implementations can include accessing, by a computing system, data that identifies a plurality of virus signatures. A signature score associated with a first virus signature can be identified that is determined based on a number of signature authorities that have submitted or endorsed the first virus signature. The computing system may determine whether the first virus signature is valid based on whether the signature score satisfies a threshold score. In response to determining that the first virus signature is valid, the first virus signature can be used by the computing system to perform virus scans of one or more files maintained by the computing system.

Antivirus signature distribution with distributed ledger

This document generally describes systems, methods, devices, and other techniques for using distributed ledgers, such as a blockchain database, to facilitate secure distribution and use of 3D model files to 3D printers over a computing network. A 3D printer controller may access an electronic ledger that identifies a plurality of 3D model files that have been made available for distribution. A particular 3D model file and a secret key may be obtained by the printer, where the 3D model file is encrypted based on the secret key. The secret key can be decrypted using a private key associated with the computing device that corresponds to the public key. After decrypting the secret key, the particular 3D model file can be decrypted using the secret key, and after decrypting the particular 3D model file, the particular 3D model file can be executed on the printer to print a physical 3D object.

Secure 3D model sharing using distributed ledger

Growing community of inventors

Sophia Antipolis, France

Andrea Maria Milazzo