US 11,861,032 B2
Adaptive differentially private count
Liam Damewood, Walnut Creek, CA (US); Oana Niculaescu, Berkeley, CA (US); Alexander Rozenshteyn, Berkeley, CA (US); and Ann Yang, Berkeley, CA (US)
Assigned to Snowflake Inc., Bozeman, MT (US)
Filed by Snowflake Inc., Bozeman, MT (US)
Filed on Apr. 6, 2022, as Appl. No. 17/714,785.
Application 17/714,785 is a continuation of application No. 17/173,936, filed on Feb. 11, 2021, granted, now 11,328,084.
Claims priority of provisional application 62/975,160, filed on Feb. 11, 2020.
Prior Publication US 2022/0229931 A1, Jul. 21, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 16/245 (2019.01); G06F 21/62 (2013.01)
CPC G06F 21/6227 (2013.01) [G06F 16/245 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method for performing an adaptive differentially private count operation on a set of data stored by a database, the method comprising:
receiving a request from a client device to perform a query on the set of data stored by the database, the request identifying a target accuracy and a maximum privacy spend;
performing a count operation on the set of data to produce a result;
perturbing the result to produce a differentially private result using a noise value, the noise value being sampled from a first probability distribution based on a fractional privacy spend, the fractional privacy spend comprising a first fraction of the maximum privacy spend;
iteratively calibrating the noise value of the differentially private result based on a secondary distribution different from the first probability distribution and based on a new fractional privacy spend, the new fractional privacy spend comprising a second fraction of the maximum privacy spend, the new fractional privacy spend being larger than fractional privacy spends of preceding iterations, the iterations of calibrating the noise value occurring until
a relative error of the differentially private result is less than or equal to the target accuracy; and
sending, to the client device, the differentially private result.