Smoothed Differential Privacy
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Differential privacy (DP) is a widely-accepted and widely-applied notion of privacy based on worst-case analysis. Often, DP classifies most mechanisms without external noise as non-private [Dwork et al., 2014], and external noises, such as Gaussian noise or Laplacian noise [Dwork et al., 2006], are introduced to improve privacy. In many real-world applications, however, adding external noise is undesirable and sometimes prohibited. For example, presidential elections often require a deterministic rule to be used [Liu et al., 2020], and small noises can lead to dramatic decreases in the prediction accuracy of deep neural networks, especially the underrepresented classes [Bagdasaryan et al., 2019]. In this paper, we propose a natural extension and relaxation of DP following the worst average-case idea behind the celebrated smoothed analysis [Spielman and Teng, 2004]. Our notion, the smoothed DP, can effectively measure the privacy leakage of mechanisms without external noises under realistic settings. We prove several strong properties of the smoothed DP, including composability, robustness to post-processing and etc. We proved that any discrete mechanism with sampling procedures is more private than what DP predicts. In comparison, many continuous mechanisms with sampling procedures are still non-private under smoothed DP. Experimentally, we first verified that the discrete sampling mechanisms are private in real-world elections. Then, we apply the smoothed DP notion on quantized gradient descent, which indicates some neural networks can be private without adding any extra noises. We believe that these results contribute to the theoretical foundation of realistic privacy measures beyond worst-case analysis.
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