k-Anonymity and ε-differential privacy are two mainstream privacy models, the former introduced to anonymize data sets and the latter to limit the knowledge gain that results from the inclusion of one individual in the data set. Whereas basic k-anonymity only protects against identity disclosure, t-closeness was presented as an extension of k-anonymity that also protects against attribute discl...

New privacy regulations together with everincreasing data availability and computational power have created a huge interest in data privacy research. One major research direction is built around k-anonymity property and its extensions, which are required for the released data. In this paper we present such an extension to k-anonymity, called psensitive k-anonymity, which solves some of the weak...

We consider the problem of releasing a table containing personal records, while ensuring individual privacy and maintaining data integrity to the extent possible. One of the techniques proposed in the literature is k-anonymization. A release is considered k-anonymous if the information corresponding to any individual in the release cannot be distinguished from that of at least k − 1 other indiv...

Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called k-anonymity has gained popularity. In a k-anonymized dataset, each record is indistinguishable from at least k−1 other records with respect to certain “identifying” attributes. In this paper we show with two simple attacks that a k-ano...

Anonymity is important in a peer-to-peer system to protect peers that offer/request services. We propose an anonymity scheme on Chord to provide a peer k-anonymity protection against a global passive adversary who can sniff all the communication on a network. For collaborating adversaries, anonymity is protected as long as they perform only passive attacks. An encryption scheme ensures that pee...