Emc Corporation

Emc IP Holding Company LLC

Dell Products Inc.

Other

Data General Corporation

Dell & Products L.p.

Emc Cororation

Stephen J Todd

Michael Kilian

Tom Teugels

Mark A Parenti

Andreas L Bauer

Douglas A Wood

Jan Van Riel

Said Tabet

Michel F Fisher

Assaf Natanzon

Philippe Armangau

Jason Alan Shepherd

Kenneth Durazzo

Wayne A Pauley, Jr

Steven M Blumenau

David I Dietrich

Uday K Gupta

Mark A O'Connell

Pengfei Wu

Nicole Reineke

Scott R Ostapovicz

Yu Cao

Sudhir Vijendra

Qiyan Chen

Dragan Savic

Brian A Castelli

Kun Wang

Robert Anthony Lincourt, Jr

Aurelian Dumitru

Nikhil Sharma

Donagh A Buckley

John Roese

Jeffrey Norton

Zoran Cakeljic

Chenhui Fan

Eloy Francisco Macha

Kim Marivoet

Gaurav Chawla

Jeroen Mattijs Van Rotterdam

Amihai Savir

Walter A O'Brien, Iii

David Stephen Reiner

Jeffrey Alan Brown

Ziye Yang

Michael Robillard

Xiaoyan Guo

Robert C Solomon

Dong Xiang

Tianqing Wang

Mohamed Sohail

John Daniel Hushon

Lintao Wan

John Philip Bell, Ii

Walter T Dobberpuhl

Paul M Bober

Mikhail Danilov

Jimmy Doyle Pike

Victor Fong

Yael Villa

Timothy J Cox

Nihar Nanda

Xuebin He

Qing Ye

James W Espy

Riaz Zolfonoon

Joseph T Frank

Brian R Tetreault

Matthew J D'Errico

George M Ericson

Sheppard David Narkier

John D Hushon, Jr

Charles H Hopkins

Brian James Martin

Ari Juels

Kai Chen

Hanna Yehuda

Gregory William Lazar

Rui Chen

Si Ruo Chen

Stephen Glen Graham

Peter John McCann

Scott Auchmoody

David W DesRoches

Zhenzhen Lin

Feng Guo

Eric Joseph Bruno

Layne Lin Peng

John Michael Hayden

Zhe Dong

Oshry Ben-Harush

Jun Tao

David E Frattura

Jidong Chen

Lejin Du

Robert A Lincourt

Simon Tao

Joseph P DeYesso

Karthikeyan Srinivasan

Nalinkumar Mistry

Humberto Rodriguez

One example method may be performed in the context of a data confidence fabric (DCF) and may include monitoring a data stream handled by a node, obtaining confidence information concerning the node, determining a data confidence score for the node, mapping the data confidence score to a security policy, and applying the security policy to the data stream. The particular security policy applied to a data stream can be varied in real time based on conditions at the node.

DCF-driven adaptive security policies

One example method includes receiving, at a node of a data confidence fabric, data from a data source, determining, by the node, whether the data source is listed in a portfolio of data sources, when the data source is listed in the portfolio, processing, by the node, the data, when the data source is not listed in the portfolio, performing, by the node, an analysis of the data, when the analysis reveals that the data is recognized by the node, processing, by the node, the data, and when the analysis reveals that the data is not recognized by the node, performing an action.

Application-aware DCF data authentication

One example method includes evaluating a function invoked by a request that is received at a local classical computing execution environment, and the request also implies performance of a quantum computing function in a quantum computing execution environment, based on an outcome of the evaluating, determining whether or not the function should be run in the local classical computing execution environment, or whether the function should be run in a separate classical computing execution environment, and when the determining indicates that the function should be run in the separate classical computing execution environment, forwarding the request to the separate classical computing environment for execution of the function. The local classical computing execution environment, the separate classical computing execution environment, and the quantum computing execution environment, are respective first, second, and third, tiers of a hybrid computing execution environment.

3-tier quantum computing execution model

A data confidence fabric allows data to be associated with confidence scores that reflect how the data may be trusted. Sensors or other devices may generate data. Confidence scores for the sensors are determined and the data confidence fabric determines a security strategy for the data based on the confidence score of the sensor generating the data. Confidence information associated with the data may include an annotation reflecting the security strategy that was applied and which may impact the confidence score of the data.

Fine-grained segmentation and traffic isolation in data confidence fabric networks

One method includes obtaining a snapshot of a data confidence fabric billing manifest, broadcasting the snapshot to one or more nodes of the data confidence fabric, deploying, at the nodes, billing logic included in the data confidence fabric billing manifest, and executing the billing logic to obtain information concerning a trust service provided regarding data associated with the nodes. The trust service may include trust annotating the data with an assessment as to the relative trustworthiness of the data.

Smart contract deployment for DCF trust services billing

One example method includes receiving, by a trust provider from a third party proxy, an indication that the third party proxy has instantiated a smart contract on behalf of the trust provider, connecting wallets of a customer to an agent of a data confidence fabric, in response to a request from an application, providing a trust service that includes processing customer data by applying a trust function to the customer data, creating annotations for the customer data based on the trust function, providing the processed customer data to a recipient, calling the smart contract using successive ones of the customer wallets until the smart contract is successfully executed, transferring, from the customer wallet that resulted in successful execution of the smart contract to the third party proxy, a trust provider fee, and transferring the trust provider fee from a third party proxy wallet to a trust provider wallet.

Third-party proxies for crypto-based DCF

One example method includes receiving a request to perform a data process, determining, by a trustworthy energy awareness (TEA) module, based on energy information and user constraints, a best time to perform the data process, and conveying, by the TEA module to a recipient, information indicating the best time to perform the data process. The data process may be a data backup process, or a garbage collection process, for example.

Data maintenance and deletion with trustworthy energy awareness

One example method includes receiving a user request for a data set, and the user request includes information concerning user requirements for the data set, identifying data records that satisfy one or more of the user requirements, calculating a respective relative value for each of the data records, and the relative values are based in part on the user requirements, and providing access controls for each data record that enable tracing of accesses of the data record.

Method of determining data set membership and delivery

One example method includes determining that an application is running on a node of a data confidence fabric; performing an investigation to determine if the application is new to the data confidence fabric; when it is determined that the application is new to the data confidence fabric, generating an update comprising metadata that identifies the application and the node on which the application is running; and automatically deploying, to the node of the data confidence fabric, an application-specific plug-in configured to annotate data received from the application that was determined to be new to the data confidence fabric.

Application discovery and data confidence fabric (DCF) overlay

One example method includes receiving, by a hybrid classical-quantum computing system, data from a node of a data confidence fabric, processing the data to create processed data, generating one or more confidence scores relating to the processed data, and making the one or more confidence scores and the processed data available to an end user. The hybrid classical-quantum computing system may also be a node of the data confidence fabric and may perform classical and/or quantum computing operations on the data.

Classical-quantum data confidence fabric

One example method includes receiving a hybrid/classical algorithm, determining a runtime characteristic of the hybrid/classical algorithm, based on the runtime characteristic, checking a memory availability for execution of the hybrid/classical algorithm, when adequate memory is not available to support execution of the hybrid/classical algorithm, modifying a classical/quantum memory fabric to provide enough memory to support execution of the hybrid/classical algorithm, and orchestrating the hybrid classical/quantum algorithm to an execution environment that includes the classical/quantum memory fabric.

Intelligent classical/quantum extended memory fabric

One example method includes receiving an orchestration automation request for an asset, identifying an ethics rule that applies to the asset, comparing the ethics rule to asset values contained in an AI ethics datastore, based on the comparing, identifying a list of assets that conform to the ethics rule, and when the asset appears in the list of assets that conform to the ethics rule, automatically placing the asset at an entity of a computing infrastructure.

AI ethics scores in automated orchestration decision-making

Generating policy-based confidence scores for data is disclosed. Data captured by a data confidence fabric is annotated when the data is created, mutated, transited or otherwise handled in the data confidence fabric. The annotations are weighted by a policy to generate policy-based confidence scores. The policy-based confidence scores are used in determining whether the data is sufficiently trusted for use by an application.

Data confidence fabric policy-based scoring

One example method includes performing a secure boot of hardware at a node of a data confidence fabric, creating an artifact that includes information concerning the secure boot, storing the artifact, receiving a data stream at the node, annotating data of the data stream with trust metadata, and associating the artifact with the data. An immutable ledger entry may be created that includes a pointer to the data, and a pointer to the artifact, and the immutable ledger entry may be accessible by an application.

Secure enclave pathing configuration for data confidence fabrics

One example method includes determining a spatial distribution of devices in a three dimensional physical environment, determining a polling frequency for each of the devices, receiving data generated by the devices, evaluating the data, and assigning a trust score to the data. The devices may take the form of respective sensors operable to sense a physical attribute of the three dimensional physical environment. A Fourier transform may be employed to determine the polling frequency.

Increasing device data confidence and operation via temporal and spatial analytics

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North Andover, MA, United States of America

Stephen J Todd