TY - JOUR T1 - Anonymity preserving sequential pattern mining JF - Artif. Intell. Law Y1 - 2014 A1 - Anna Monreale A1 - Dino Pedreschi A1 - Ruggero G. Pensa A1 - Fabio Pinelli AB - 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. In this paper we propose to apply the Privacy-by-design paradigm for designing a technological framework to counter the threats of undesirable, unlawful effects of privacy violation on sequence data, without obstructing the knowledge discovery opportunities of data mining technologies. 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, together with several basic statistics and other analytical properties of the original data, including the clustering structure. A comprehensive experimental study on realistic datasets of process-logs, web-logs and GPS tracks is carried out, which empirically shows how, in our proposed method, the protection of privacy meets analytical utility. VL - 22 UR - http://dx.doi.org/10.1007/s10506-014-9154-6 ER - TY - Generic T1 - Anonymous Sequences from Trajectory Data T2 - 17th Italian Symposium on Advanced Database Systems Y1 - 2009 A1 - Ruggero G. Pensa A1 - Anna Monreale A1 - Fabio Pinelli A1 - Dino Pedreschi JF - 17th Italian Symposium on Advanced Database Systems CY - Camogli, Italy ER - TY - CONF T1 - Social Network Analysis as Knowledge Discovery Process: A Case Study on Digital Bibliography T2 - ASONAM Y1 - 2009 A1 - Michele Coscia A1 - Fosca Giannotti A1 - Ruggero G. Pensa JF - ASONAM ER - TY - CONF T1 - Social Network Analysis as Knowledge Discovery Process: A Case Study on Digital Bibliography T2 - ASONAM Y1 - 2009 A1 - Michele Coscia A1 - Fosca Giannotti A1 - Ruggero G. Pensa JF - ASONAM ER - TY - CONF T1 - Pattern-Preserving k-Anonymization of Sequences and its Application to Mobility Data Mining T2 - PiLBA Y1 - 2008 A1 - Ruggero G. Pensa A1 - Anna Monreale A1 - Fabio Pinelli A1 - Dino Pedreschi AB - Sequential pattern mining is a major research field in knowledge discovery and data mining. Thanks to the increasing availability of transaction data, it is now possible to provide new and improved services based on users’ and customers’ behavior. However, this puts the citizen’s privacy at risk. Thus, it is important to develop new privacy-preserving data mining techniques that do not alter the analysis results significantly. In this paper we propose a new approach for anonymizing sequential data by hiding infrequent, and thus potentially sensible, subsequences. Our approach guarantees that the disclosed data are k-anonymous and preserve the quality of extracted patterns. An application to a real-world moving object database is presented, which shows the effectiveness of our approach also in complex contexts. JF - PiLBA UR - https://air.unimi.it/retrieve/handle/2434/52786/106397/ProceedingsPiLBA08.pdf#page=44 ER -