论文标题
旨在评估当地隐私模型中的重新识别风险
Toward Evaluating Re-identification Risks in the Local Privacy Model
论文作者
论文摘要
新民党（当地差异隐私）最近引起了广泛关注，因为数据隐私的指标可以防止本地模型中混淆数据中的个人数据推断。但是，在某些情况下，对手希望执行重新识别攻击，以将混淆的数据与此模型中的用户联系起来。在这些情况下，自然地党会导致过度混淆并破坏效用，因为它并非旨在直接防止重新识别。在本文中，我们提出了一种重新识别风险的度量，我们称之为PIE（个人信息熵）。该派的设计使其直接防止本地模型中的重新识别攻击。它降低了对手的最低识别误差概率（即贝叶斯误差概率）。我们分析了开发卢比与PIE之间的关系，并分析了提供LDP的两种混淆机制的分布估算中的PIE和实用性。通过实验，我们表明，当我们将重新识别视为隐私风险时，最不发达国家会导致过度混淆并破坏公用事业。然后，我们表明该派可用于保证当地混淆机制的低重新识别风险，同时保持高效用。
LDP (Local Differential Privacy) has recently attracted much attention as a metric of data privacy that prevents the inference of personal data from obfuscated data in the local model. However, there are scenarios in which the adversary wants to perform re-identification attacks to link the obfuscated data to users in this model. LDP can cause excessive obfuscation and destroy the utility in these scenarios because it is not designed to directly prevent re-identification. In this paper, we propose a measure of re-identification risks, which we call PIE (Personal Information Entropy). The PIE is designed so that it directly prevents re-identification attacks in the local model. It lower-bounds the lowest possible re-identification error probability (i.e., Bayes error probability) of the adversary. We analyze the relation between LDP and the PIE, and analyze the PIE and utility in distribution estimation for two obfuscation mechanisms providing LDP. Through experiments, we show that when we consider re-identification as a privacy risk, LDP can cause excessive obfuscation and destroy the utility. Then we show that the PIE can be used to guarantee low re-identification risks for the local obfuscation mechanisms while keeping high utility.
