Avoiding Floating-Point Side Channels in the Report Noisy Max with Gap Mechanism

The Noisy Max mechanism and its variations are fundamental private selection algorithms that are used to select items from a set of candidates (such as the most common diseases in a population), while controlling the privacy leakage in the underlying data. A recently proposed extension, Noisy Top-k with Gap, provides numerical information about how much better the selected items are compared to the non-selected items (e.g., how much more common are the selected diseases). This extra information comes at no privacy cost but crucially relies on infinite precision for the privacy guarantees. In this paper, we provide a finite-precision secure implementation of this algorithm that takes advantage of integer arithmetic.

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Publication Details

Journal
Journal of Privacy and Confidentiality
Published
2025-12-31
DOI
https://doi.org/10.29012/jpc.894
Primary Topic
Cryptography and Data Security
Type
article
Field-Weighted Citation Impact
0.00

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article

Avoiding Floating-Point Side Channels in the Report Noisy Max with Gap Mechanism

Daniel Kifer, Yuxin Wang, Yingtai Xiao, Prottay Protivash et al.
Journal of Privacy and Confidentiality
Cryptography and Data Security
article

Avoiding Floating-Point Side Channels in the Report Noisy Max with Gap Mechanism

Daniel Kifer, Yuxin Wang, Yingtai Xiao, Prottay Protivash, Zeyu Ding, Danfeng Zhang, John Durrell, Guanhong Wang
article en

Abstract

The Noisy Max mechanism and its variations are fundamental private selection algorithms that are used to select items from a set of candidates (such as the most common diseases in a population), while controlling the privacy leakage in the underlying data. A recently proposed extension, Noisy Top-k with Gap, provides numerical information about how much better the selected items are compared to the non-selected items (e.g., how much more common are the selected diseases). This extra information comes at no privacy cost but crucially relies on infinite precision for the privacy guarantees. In this paper, we provide a finite-precision secure implementation of this algorithm that takes advantage of integer arithmetic.

Journal of Privacy and ConfidentialityVol. 15(3)
Binghamton University (US), Pennsylvania State University (US), University of Maryland, College Park (US)
National Science Foundation
Peace, Justice and strong institutions
Openalex Percentile: Top 17%
Cryptography and Data Security
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Avoiding Floating-Point Side Channels in the Report Noisy Max with Gap Mechanism — Daniel Kifer, Yuxin Wang, et al. · Journal of Privacy and Confidentiality (2025) | TGRS Research Map | TGRS