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US Patent 10229287 Differentially private processing and database storage

Patent 10229287 was granted and assigned to LeapYear Technologies on March, 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
LeapYear Technologies
LeapYear Technologies
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Current Assignee
LeapYear Technologies
LeapYear Technologies
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
102292870
Patent Inventor Names
Mihai Maruseac0
Christopher Hockenbrocht0
Ishaan Nerurkar0
Liam Damewood0
Alexander Rozenshteyn0
Date of Patent
March 12, 2019
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Patent Application Number
157939070
Date Filed
October 25, 2017
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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 11055432 Budget tracking in a differentially private database system
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US Patent 11755769 Differentially private query budget refunding
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US Patent 11328084 Adaptive differentially private count
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US Patent 11861032 Adaptive differentially private count
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US Patent 11893133 Budget tracking in a differentially private database system
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US Patent 12019779 Differentially private processing and database storage
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US Patent 12072998 Differentially private processing and database storage
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...
Patent Primary Examiner
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Robert B Leung
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Patent abstract

A hardware database privacy device is communicatively coupled to a private database system. The hardware database privacy device receives a request from a client device to perform a query of the private database system and identifies a level of differential privacy corresponding to the request. The identified level of differential privacy includes privacy parameters (ε,δ) indicating the degree of information released about the private database system. The hardware database privacy device identifies a set of operations to be performed on the set of data that corresponds to the requested query. After the set of data is accessed, the set of operations is modified based on the identified level of differential privacy such that a performance of the modified set of operations produces a result set that is (ε,δ)-differentially private.

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