噪声列重复下的种子数据库匹配
Seeded Database Matching Under Noisy Column Repetitions
摘要：
通过与公开可用的相关用户数据匹配来从匿名化数据中重新识别或去匿名化用户已经引起了隐私问题，导致除了匿名化之外的混淆的补充措施。最近的研究提供了一个基本的了解条件下，隐私攻击是成功的，无论是在存在混淆或同步错误，从时间索引数据库的采样。本文提出了一个统一的框架，同时考虑混淆和同步错误，并研究了噪声列重复下的数据库匹配。通过设计副本检测和种子删除检测算法，并使用信息论工具，成功匹配的充分条件。结果表明，种子大小的行大小的对数是足够的，以保证所有删除的列的检测。还证明了这个充分条件是必要的，从而表征了有噪列重复下数据库匹配的数据库匹配能力，并为匿名和模糊时间索引数据的隐私保护发布提供了见解。
英文摘要：
The re-identification or de-anonymization of users from anonymized data through matching with publicly-available correlated user data has raised privacy concerns, leading to the complementary measure of obfuscation in addition to anonymization. Recent research provides a fundamental understanding of the conditions under which privacy attacks are successful, either in the presence of obfuscation or synchronization errors stemming from the sampling of time-indexed databases. This paper presents a unified framework considering both obfuscation and synchronization errors and investigates the matching of databases under noisy column repetitions. By devising replica detection and seeded deletion detection algorithms, and using information-theoretic tools, sufficient conditions for successful matching are derived. It is shown that a seed size logarithmic in the row size is enough to guarantee the detection of all deleted columns. It is also proved that this sufficient condition is necessary, thus characterizing the database matching capacity of database matching under noisy column repetitions and providing insights on privacy-preserving publication of anonymized and obfuscated time-indexed data.
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