Distribution-Agnostic Database De-Anonymization Under Obfuscation And Synchronization Errors
arxiv(2024)
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.
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