Practical Differentially Private Top-k Selection with Pay-what-you-get Composition

05/10/2019
by   David Durfee, et al.
0

We study the problem of top-k selection over a large domain universe subject to user-level differential privacy. Typically, the exponential mechanism or report noisy max are the algorithms used to solve this problem. However, these algorithms require querying the database for the count of each domain element. We focus on the setting where the data domain is unknown, which is different than the setting of frequent itemsets where an apriori type algorithm can help prune the space of domain elements to query. We design algorithms that ensures (approximate) (ϵ,δ>0)-differential privacy and only needs access to the true top-k̅ elements from the data for any chosen k̅≥ k. This is a highly desirable feature for making differential privacy practical, since the algorithms require no knowledge of the domain. We consider both the setting where a user's data can modify an arbitrary number of counts by at most 1, i.e. unrestricted sensitivity, and the setting where a user's data can modify at most some small, fixed number of counts by at most 1, i.e. restricted sensitivity. Additionally, we provide a pay-what-you-get privacy composition bound for our algorithms. That is, our algorithms might return fewer than k elements when the top-k elements are queried, but the overall privacy budget only decreases by the size of the outcome set.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/28/2022

A Joint Exponential Mechanism For Differentially Private Top-k

We present a differentially private algorithm for releasing the sequence...
research
08/24/2023

Counting Distinct Elements Under Person-Level Differential Privacy

We study the problem of counting the number of distinct elements in a da...
research
09/19/2021

Making the Most of Parallel Composition in Differential Privacy

We show that the `optimal' use of the parallel composition theorem corre...
research
11/08/2019

The Complexity of Verifying Loop-free Programs as Differentially Private

We study the problem of verifying differential privacy for loop-free pro...
research
01/31/2023

Tight Data Access Bounds for Private Top-k Selection

We study the top-k selection problem under the differential privacy mode...
research
10/17/2020

Locally Differentially Private Analysis of Graph Statistics

Differentially private analysis of graphs is widely used for releasing s...
research
04/29/2019

Free Gap Information from the Differentially Private Sparse Vector and Noisy Max Mechanisms

Noisy Max and Sparse Vector are selection algorithms for differential pr...

Please sign up or login with your details

Forgot password? Click here to reset