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US Patent 11861032 Adaptive differentially private count

Patent 11861032 was granted and assigned to Snowflake (Real Estate Company) on January, 2024 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
Snowflake (Real Estate Company)
Snowflake (Real Estate Company)
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Current Assignee
Snowflake (Real Estate Company)
Snowflake (Real Estate Company)
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
118610320
Patent Inventor Names
Liam Damewood0
Alexander Rozenshteyn0
Oana Niculaescu0
Ann Yang0
Date of Patent
January 2, 2024
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Patent Application Number
177147850
Date Filed
April 6, 2022
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Patent Citations
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US Patent 9094378 Homomorphic cryptography on numerical values in digital computing
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US Patent 9244976 Just-in-time analytics on large file systems and hidden databases
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US Patent 9384226 Personal content item searching system and method
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US Patent 10192069 Differentially private processing and database storage
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US Patent 10229287 Differentially private processing and database storage
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US Patent 10642847 Differentially private budget tracking using Renyi divergence
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US Patent 11328084 Adaptive differentially private count
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US Patent 10733320 Differentially private processing and database storage
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...
Patent Citations Received
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US Patent 12105832 Adaptive differentially private count
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Patent Primary Examiner
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Alicia M Willoughby
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CPC Code
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G06F 21/6227
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G06F 16/245
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Patent abstract

A differentially private security system communicatively coupled to a database storing restricted data receives a database query from a client. The database query includes an operation, a target accuracy, and a maximum privacy spend for the query. The system performs the operation to produce a result, then injects the result with noise sampled from a Laplace distribution to produce a differentially private result. The system iteratively calibrates the noise value of the differentially private result using a secondary distribution different from the Laplace distribution and a new fractional privacy spend. The system ceases to iterate when an iteration uses the maximum privacy spend or a relative error of the differentially private result is determined to satisfy the target accuracy, or both. The system sends the differentially private result to the client.

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