2012 · 205 citations · 5 references
ComputingComputer ScienceData ScienceData MiningDatabasesUncertainty QuantificationData AnonymizationData ConfidentialityData PrivacyData ProtectionData SecurityDifferential PrivacyAnonymizationAnonymization AlgorithmsK-anonymityMicrodata AnonymizationPrivacy-preserving Data AnalysisPrivacy-preserving Data CollectionPrivacy-utility TradeoffPrivate Data Publishing
k‑Anonymization methods have been studied extensively in the database community, yet they are known to lack strong privacy guarantees. The paper investigates two questions: what formal privacy guarantee, if any, k‑anonymization provides, and whether exploiting the adversary’s uncertainty about the data can yield a meaningful relaxation of differential privacy. The authors elaborate on these two motivations in detail.
This paper aims at answering the following two questions in privacy-preserving data analysis and publishing. The first is: What formal privacy guarantee (if any) does k-anonymization methods provide? k-Anonymization methods have been studied extensively in the database community, but have been known to lack strong privacy guarantees. The second question is: How can we benefit from the adversary's uncertainty about the data? More specifically, can we come up a meaningful relaxation of differential privacy [2, 3] by exploiting the adversary's uncertainty about the dataset? We now discuss these two motivations in more detail.
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Pierangela Samarati, Latanya Sweeney · Research Showcase @ Carnegie Mellon University (Carnegie Mellon University) · 2018 · 1.3K citations · Full text