PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Pensa R. R., Monreale A., Pinelli F., Pedreschi D. Anonymous sequences from trajectory data. In: SEBD 2009 - The 17th Italian Symposium on Advanced Database Systems (Camogli (GE), 21-24 June 2009). Atti, pp. 361 - 372. Seneca Edizioni, 2009.
 
 
Abstract
(English)
The increasing availability of personal data of a sequential nature, such as time-stamped transaction or location data, enables increasingly sophisticated sequential pattern mining techniques. However, privacy is at risk if it is possible to reconstruct the identity of individuals from sequential data. Therefore, it is important to develop privacy- preserving techniques that support publishing of really anonymous data, without altering the analysis results significantly. First, we introduce a k-anonymity framework for sequence data, by defining the sequence linking attack model and its associated countermeasure, a k-anonymity notion for sequence datasets, which provides a formal protection against the attack. Second, we instantiate this framework and provide a specific method for constructing the k-anonymous version of a sequence dataset, which preserves the results of sequential pattern mining. A comprehensive experimental study on realistic GPS data is carried out, which empirically shows how the protection of privacy meets analytical utility.
Subject Privacy
Sequential Pattern mining
K.4.1 Public Policy Issues. Privacy


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