Smooth Sensitivity for Geo-Privacy

• URL: http://arxiv.org/abs/2405.06307v1
• Date: Fri, 10 May 2024 08:32:07 GMT
• Title: Smooth Sensitivity for Geo-Privacy
• Authors: Yuting Liang, Ke Yi,
• Abstract summary: Local model of differential privacy (LDP) is the predominant model under which the problem has been studied. Geo-Privacy (GP) stipulates that the level of distinguishability be proportional to $mathrmdist(x_i, x_i')$. We generalize the smooth sensitivity framework from Differential Privacy to Geo-Privacy, which allows us to add noise tailored to the hardness of the given instance.
• Score: 17.835910182900985
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Suppose each user $i$ holds a private value $x_i$ in some metric space $(U, \mathrm{dist})$, and an untrusted data analyst wishes to compute $\sum_i f(x_i)$ for some function $f : U \rightarrow \mathbb{R}$ by asking each user to send in a privatized $f(x_i)$. This is a fundamental problem in privacy-preserving population analytics, and the local model of differential privacy (LDP) is the predominant model under which the problem has been studied. However, LDP requires any two different $x_i, x'_i$ to be $\varepsilon$-distinguishable, which can be overly strong for geometric/numerical data. On the other hand, Geo-Privacy (GP) stipulates that the level of distinguishability be proportional to $\mathrm{dist}(x_i, x_i')$, providing an attractive alternative notion of personal data privacy in a metric space. However, existing GP mechanisms for this problem, which add a uniform noise to either $x_i$ or $f(x_i)$, are not satisfactory. In this paper, we generalize the smooth sensitivity framework from Differential Privacy to Geo-Privacy, which allows us to add noise tailored to the hardness of the given instance. We provide definitions, mechanisms, and a generic procedure for computing the smooth sensitivity under GP equipped with a general metric. Then we present three applications: one-way and two-way threshold functions, and Gaussian kernel density estimation, to demonstrate the applicability and utility of our smooth sensitivity framework.

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