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US Patent 11698990 Computer-implemented privacy engineering system and method

Patent 11698990 was granted and assigned to Privitar Limited on July, 2023 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent
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Patent attributes

Patent Applicant
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Privitar Limited
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Current Assignee
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Privitar Limited
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
116989900
Date of Patent
July 11, 2023
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Patent Application Number
160974820
Date Filed
May 2, 2017
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Patent Citations
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US Patent 8670563 System and method for designing secure client-server communication protocols based on certificateless public key infrastructure
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US Patent 8627107 System and method of securing private health information
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Patent Citations Received
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US Patent 12130942 Budget tracking in a differentially private database system
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US Patent 12079351 Application of trained artificial intelligence processes to encrypted data within a distributed computing environment
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US Patent 12008103 Method and apparatus for detecting malware via analysis of a screen capture
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US Patent 11960624 Systems and methods to enhance privacy through decision tree based suppression rules on relational databases
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US Patent 11893133 Budget tracking in a differentially private database system
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US Patent 11843576 Methods and apparatus to perform network-based monitoring of media accesses
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US Patent 11783077 Systems and methods for privacy-enhancing modification of a database query
Patent Primary Examiner
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Oleg Korsak
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CPC Code
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H04L 9/3247
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H04L 9/0825
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H04L 9/008
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H04L 9/0844
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G06F 21/602
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G06F 21/6254
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G06F 2211/007
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G06F 2212/402
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A system allows the identification and protection of sensitive data in a multiple ways, which can be combined for different workflows, data situations or use cases. The system scans datasets to identify sensitive data or identifying datasets, and to enable the anonymisation of sensitive or identifying datasets by processing that data to produce a safe copy. Furthermore, the system prevents access to a raw dataset. The system enables privacy preserving aggregate queries and computations. The system uses differentially private algorithms to reduce or prevent the risk of identification or disclosure of sensitive information. The system scales to big data and is implemented in a way that supports parallel execution on a distributed compute cluster.

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