Global privacy guarantee in serial data publishing

While previous works on privacy-preserving serial data publishing consider the scenario where sensitive values may persist over multiple data releases, we find that no previous work has sufficient protection provided for sensitive values that can change over time, which should be the more common case. In this work, we propose to study the privacy guarantee for such transient sensitive values, which we call the global guarantee. We formally define the problem for achieving this guarantee. We show that the data satisfying the global guarantee also satisfies a privacy guarantee commonly adopted in the privacy literature called the local guarantee.

[1]  Raymond Chi-Wing Wong,et al.  (α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing , 2006, KDD '06.

[2]  Benjamin C. M. Fung,et al.  Anonymizing sequential releases , 2006, KDD '06.

[3]  Ke Wang,et al.  Anonymization for Global Privacy Guarantee in Serial Data Publishing , 2010, ICDE 2010.

[4]  Elisa Bertino,et al.  Secure Anonymization for Incremental Datasets , 2006, Secure Data Management.

[5]  Jian Pei,et al.  Anonymity for continuous data publishing , 2008, EDBT '08.

[6]  Yufei Tao,et al.  M-invariance: towards privacy preserving re-publication of dynamic datasets , 2007, SIGMOD '07.

[7]  Raymond Chi-Wing Wong,et al.  Privacy preserving serial data publishing by role composition , 2008, Proc. VLDB Endow..

[8]  Ninghui Li,et al.  t-Closeness: Privacy Beyond k-Anonymity and l-Diversity , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[9]  Jian Pei,et al.  Maintaining K-Anonymity against Incremental Updates , 2007, 19th International Conference on Scientific and Statistical Database Management (SSDBM 2007).

[10]  Ashwin Machanavajjhala,et al.  l-Diversity: Privacy Beyond k-Anonymity , 2006, ICDE.

[11]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..