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US Patent 10198399 Cryptographically secure machine learning

Patent 10198399 was granted and assigned to KenSci on February, 2019 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
KenSci
KenSci
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Current Assignee
KenSci
KenSci
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
101983990
Patent Inventor Names
Kyle Josiah Fritchman0
Tyler John Hughes0
Anderson Nascimento0
Ankur Teredesai0
Martine Ivonne Leo De Cock0
Date of Patent
February 5, 2019
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Patent Application Number
159138640
Date Filed
March 6, 2018
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Patent Citations Received
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US Patent 12105813 Secure on-premises to cloud connector framework
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US Patent 11855970 Systems and methods for blind multimodal learning
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US Patent 11991156 Systems and methods for secure averaging of models for federated learning and blind learning using secure multi-party computation
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US Patent 12002309 Systems and methods for managing vehicle data
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US Patent 12001965 Distributed privacy-preserving computing on protected data
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US Patent 12081410 Network entity for determining a model for digitally analyzing input data
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US Patent 12088565 Systems and methods for privacy preserving training and inference of decentralized recommendation systems from decentralized data
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US Patent 12107837 Cloud based machine learning notebook data loss prevention
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...
Patent Primary Examiner
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Dave Misir
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Patent abstract

Embodiments are directed towards classifying data. A machine learning (ML) engine may select an ML model that may employ a cryptographic multi-party computation (MPC) protocol based on model preferences, including a parameter model, provided by a client. A randomness engine may be employed to provide random values and other random values based on the MPC protocol such that the random values may be provided to the client and the other random values may be provided to an answer engine. Input values that correspond to fields in the parameter model may be provided by the client such that the input values may be based on the MPC protocol and the random values. The answer engine may be employed to provide partial results to the question based on the ML model, the input values, and the MPC protocol that may be provided to the client.

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