Differentially-Private Control-Flow Node Coverage for Software Usage Analysis

Authors: 

Hailong Zhang, Sufian Latif, Raef Bassily, and Atanas Rountev, The Ohio State University

Abstract: 

There are significant privacy concerns about the collection of usage data from deployed software. We propose a novel privacy-preserving solution for a problem of central importance to software usage analysis: control-flow graph coverage analysis over many deployed software instances. Our solution employs the machinery of differential privacy and its generalizations, and develops the following technical contributions: (1) a new notion of privacy guarantees based on a neighbor relation between control-flow graphs that prevents causality-based inference, (2) a new differentially-private algorithm design based on a novel definition of sensitivity with respect to differences between neighbors, (3) an efficient implementation of the algorithm using dominator trees derived from control-flow graphs, (4) a pruning approach to reduce the noise level by tightening the sensitivity bound using restricted sensitivity, and (5) a refined notion of relaxed indistinguishability based on distances between neighbors. Our evaluation demonstrates that these techniques can achieve practical accuracy while providing principled privacy-by-design guarantees.

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BibTeX
@inproceedings {255330,
author = {Hailong Zhang and Sufian Latif and Raef Bassily and Atanas Rountev},
title = {Differentially-Private Control-Flow Node Coverage for Software Usage Analysis},
booktitle = {29th {USENIX} Security Symposium ({USENIX} Security 20)},
year = {2020},
isbn = {978-1-939133-17-5},
pages = {1021--1038},
url = {https://www.usenix.org/conference/usenixsecurity20/presentation/zhang-hailong},
publisher = {{USENIX} Association},
month = aug,
}
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