关于GDPR“挑出”概念的形式化
Towards Formalizing the GDPR's Notion of Singling Out
摘要：
围绕数据隐私的法律和数学思维之间存在着巨大的概念差距。其影响是不确定哪些技术产品充分符合法律标准中表达的期望。一连串成功的隐私攻击加剧了这种不确定性，表明传统的统计披露限制技术往往达不到法律标准所设想的那种隐私。我们定义了谓词挑出，这是一种新型的隐私攻击，旨在捕捉通用数据保护条例（GDPR）中出现的挑出概念。非正式地，如果对手谓词设法找到与一行$x \in X$精确匹配的谓词$p$，并且概率比统计基线好得多，则对手谓词使用数据发布机制$M(X)$的输出挑出数据集$X$。防止这种攻击的数据发布机制是安全的，可以防止谓词挑出（PSO安全）。我们认为，PSO安全是一个数学概念，具有法律后果。根据GDPR，任何旨在“提供匿名”个人数据的数据发布机制都必须是安全的，不能被单独挑出，因此必须是PSO安全的。然后，我们分析了PSO的安全性，表明它无法自我组合。也就是说，$ω(\log n)$精确计数的组合，每个单独的PSO安全，使攻击者能够预测单挑。事实上，仅仅两个PSO安全机制的组合可能无法提供PSO安全性。最后，我们问是否差分隐私和$k$ -匿名PSO安全。利用连接到统计泛化，我们表明，差分隐私意味着PSO安全。然而，$k$ -匿名没有：存在一个简单的和一般的谓词挑出攻击在温和的假设下，对$k$ -匿名和数据分布。
英文摘要：
There is a significant conceptual gap between legal and mathematical thinking around data privacy. The effect is uncertainty as to which technical offerings adequately match expectations expressed in legal standards. The uncertainty is exacerbated by a litany of successful privacy attacks, demonstrating that traditional statistical disclosure limitation techniques often fall short of the sort of privacy envisioned by legal standards. We define predicate singling out, a new type of privacy attack intended to capture the concept of singling out appearing in the General Data Protection Regulation (GDPR). Informally, an adversary predicate singles out a dataset $X$ using the output of a data release mechanism $M(X)$ if it manages to find a predicate $p$ matching exactly one row $x \in X$ with probability much better than a statistical baseline. A data release mechanism that precludes such attacks is secure against predicate singling out (PSO secure). We argue that PSO security is a mathematical concept with legal consequences. Any data release mechanism that purports to "render anonymous" personal data under the GDPR must be secure against singling out, and hence must be PSO secure. We then analyze PSO security, showing that it fails to self-compose. Namely, a combination of $ω(\log n)$ exact counts, each individually PSO secure, enables an attacker to predicate single out. In fact, the composition of just two PSO-secure mechanisms can fail to provide PSO security. Finally, we ask whether differential privacy and $k$-anonymity are PSO secure. Leveraging a connection to statistical generalization, we show that differential privacy implies PSO security. However, $k$-anonymity does not: there exists a simple and general predicate singling out attack under mild assumptions on the $k$-anonymizer and the data distribution.
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