Unleash the Power of Ellipsis: Accuracy-enhanced Sparse Vector Technique with Exponential Noise
Yuhan Liu, Sheng Wang, Yixuan Liu, Feifei Li, Hong Chen

TL;DR
This paper improves the Sparse Vector Technique in differential privacy by introducing exponential noise and novel correction methods, significantly enhancing accuracy and utility.
Contribution
It provides a new privacy analysis for SVT, identifies exponential noise as optimal, and develops threshold correction and appending strategies to boost performance.
Findings
Exponential noise is optimal among evaluated noise types for SVT.
Proposed methods improve accuracy metrics by up to 50%.
Theoretical and empirical results validate the effectiveness of the new approach.
Abstract
The Sparse Vector Technique (SVT) is one of the most fundamental tools in differential privacy (DP). It works as a backbone for adaptive data analysis by answering a sequence of queries on a given dataset, and gleaning useful information in a privacy-preserving manner. Unlike the typical private query releases that directly publicize the noisy query results, SVT is less informative -- it keeps the noisy query results to itself and only reveals a binary bit for each query, indicating whether the query result surpasses a predefined threshold. To provide a rigorous DP guarantee for SVT, prior works in the literature adopt a conservative privacy analysis by assuming the direct disclosure of noisy query results as in typical private query releases. This approach, however, hinders SVT from achieving higher query accuracy due to an overestimation of the privacy risks, which further leads to an…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
