局部敏感哈希并不能保证隐私！针对 Google FLoC 与 MinHash Hierarchy 系统的攻击
Locality-Sensitive Hashing Does Not Guarantee Privacy! Attacks on Google's FLoC and the MinHash Hierarchy System
AI总结：
本文通过攻击 Google FLoC 与 MinHash Hierarchy，证明仅依赖局部敏感哈希无法保证隐私，并揭示用户浏览历史与地理位置可被还原。
摘要：
近期提出的系统旨在利用局部敏感哈希实现隐私。我们展示了这些方法为何会失败：针对两个此类系统提出攻击，即 Google 用于隐私保护定向广告的 FLoC 提案，以及以隐私保护方式处理移动用户流量行为的 MinHash Hierarchy 系统。我们的攻击反驳了这些系统所声称的原像抗性、匿名性和隐私保证。在 FLoC 情形中，我们展示了如何利用 Sybil 攻击去匿名化用户，并利用生成对抗网络为 30% 的用户重建 10% 或更多的浏览历史。我们仅通过分析 FLoC 使用的哈希就实现了这一点。对于 MinHash，我们精确识别出一部分个体的移动轨迹，并且平均而言，同样仅利用哈希，就能将用户的移动范围缩小到仅占可能地理区域的 10%。此外，我们还反驳了其差分隐私声明。
英文摘要：
Recently proposed systems aim at achieving privacy using locality-sensitive hashing. We show how these approaches fail by presenting attacks against two such systems: Google's FLoC proposal for privacy-preserving targeted advertising and the MinHash Hierarchy, a system for processing mobile users' traffic behavior in a privacy-preserving way. Our attacks refute the pre-image resistance, anonymity, and privacy guarantees claimed for these systems. In the case of FLoC, we show how to deanonymize users using Sybil attacks and to reconstruct 10% or more of the browsing history for 30% of its users using Generative Adversarial Networks. We achieve this only analyzing the hashes used by FLoC. For MinHash, we precisely identify the movement of a subset of individuals and, on average, we can limit users' movement to just 10% of the possible geographic area, again using just the hashes. In addition, we refute their differential privacy claims.
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