## Abstract

We introduce a new model of k-type anonymity, called k-concealment, as an alternative to the well-known model of k-anonymity. This new model achieves similar privacy goals as kanonymity: While in k-anonymity one generalizes the table records so that each one of them becomes equal to at least k -1 other records, when projected on the subset of quasi-identifiers, k-concealment proposes to generalize the table records so that each one of them becomes computationally - indistinguishable from at least k - 1 others. As the new model extends that of k-anonymity, it offers higher utility. To motivate the new model and to lay the ground for its introduction, we first present three other models, called (1, k)-, (k, 1)- and (k, k)-anonymity which also extend k-anonymity. We characterize the interrelation between the four models and propose algorithms for anonymizing data according to them. Since k-anonymity, on its own, is insecure, as it may allow adversaries to learn the sensitive information of some individuals, it must be enhanced by a security measure such as p-sensitivity or l-diversity. We show how also k-concealment can be enhanced by such measures. We demonstrate the usefulness of our models and algorithms through extensive experiments.

Original language | English |
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Pages (from-to) | 189-222 |

Number of pages | 34 |

Journal | Transactions on Data Privacy |

Volume | 5 |

Issue number | 1 |

State | Published - 1 Apr 2012 |

Externally published | Yes |

## ASJC Scopus subject areas

- Software
- Statistics and Probability