混淆与同步错误下与分布无关的数据库去匿名化
Distribution-Agnostic Database De-Anonymization Under Obfuscation And Synchronization Errors
AI总结：
本文在数据分布未知且存在同步错误与混淆的条件下，通过改进副本检测算法和新的种子删除检测算法，建立了数据库去匿名化的理论保证，并证明所需条件与分布已知情形同样紧致，同时通过仿真验证了算法的实际有效性。
摘要：
数据库去匿名化通常涉及将匿名化数据库与相关的公开可用数据进行匹配。现有研究要么侧重于实用层面，无需了解数据分布但提供的保证有限，要么侧重于假设分布已知的理论层面。本文旨在弥合这两种方法，在同步错误和混淆条件下，且无需数据分布先验知识的情况下，为数据库去匿名化提供理论保证。通过使用一种改进的副本检测算法和一种新的种子删除检测算法，我们建立了成功匹配所需的数据库增长率充分条件，证明相对于行大小而言，双对数规模的种子大小足以检测数据库中的删除。重要的是，我们的研究结果表明，这些充分的去匿名化条件具有紧致性，并且与分布已知情形下的条件相同，从而避免了因分布未知而导致的渐近性能损失。最后，我们通过仿真评估了所提算法的性能，证实了它们在更实际的非渐近场景中的有效性。
英文摘要：
Database de-anonymization typically involves matching an anonymized database with correlated publicly available data. Existing research focuses either on practical aspects without requiring knowledge of the data distribution yet provides limited guarantees, or on theoretical aspects assuming known distributions. This paper aims to bridge these two approaches, offering theoretical guarantees for database de-anonymization under synchronization errors and obfuscation without prior knowledge of data distribution. Using a modified replica detection algorithm and a new seeded deletion detection algorithm, we establish sufficient conditions on the database growth rate for successful matching, demonstrating a double-logarithmic seed size relative to row size is sufficient for detecting deletions in the database. Importantly, our findings indicate that these sufficient de-anonymization conditions are tight and are the same as in the distribution-aware setting, avoiding asymptotic performance loss due to unknown distributions. Finally, we evaluate the performance of our proposed algorithms through simulations, confirming their effectiveness in more practical, non-asymptotic, scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
