On sampling, anonymization, and differential privacy or, k-anonymization meets differential privacy

Authors:
Ninghui Li;Wahbeh Qardaji;Dong Su
Affiliations:
Purdue University, West Lafayette, IN;Purdue University, West Lafayette, IN;Purdue University, West Lafayette, IN
Venue:
Proceedings of the 7th ACM Symposium on Information, Computer and Communications Security
Year:
2012

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Abstract

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.