DeepAI AI Chat
Log In Sign Up

Tight Data Access Bounds for Private Top-k Selection

01/31/2023
by   Hao Wu, et al.
The University of Melbourne
0

We study the top-k selection problem under the differential privacy model: m items are rated according to votes of a set of clients. We consider a setting in which algorithms can retrieve data via a sequence of accesses, each either a random access or a sorted access; the goal is to minimize the total number of data accesses. Our algorithm requires only O(√(mk)) expected accesses: to our knowledge, this is the first sublinear data-access upper bound for this problem. Our analysis also shows that the well-known exponential mechanism requires only O(√(m)) expected accesses. Accompanying this, we develop the first lower bounds for the problem, in three settings: only random accesses; only sorted accesses; a sequence of accesses of either kind. We show that, to avoid Ω(m) access cost, supporting *both* kinds of access is necessary, and that in this case our algorithm's access cost is optimal.

READ FULL TEXT

page 1

page 2

page 3

page 4

06/17/2018

Property Testing for Differential Privacy

We consider the problem of property testing for differential privacy: wi...
02/21/2020

Locally Private Hypothesis Selection

We initiate the study of hypothesis selection under local differential p...
12/23/2022

On the Privacy-Utility Trade-off With and Without Direct Access to the Private Data

We study an information theoretic privacy mechanism design problem for t...
05/10/2019

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

We study the problem of top-k selection over a large domain universe sub...
03/05/2022

Online List Labeling: Breaking the log^2n Barrier

The online list labeling problem is an algorithmic primitive with a larg...
02/11/2023

On Differential Privacy and Adaptive Data Analysis with Bounded Space

We study the space complexity of the two related fields of differential ...
05/19/2023

Differentially Private Online Item Pricing

This work addresses the problem of revenue maximization in a repeated, u...