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Artificial Intelligence and Law

, Volume 22, Issue 2, pp 141–173 | Cite as

Anonymity preserving sequential pattern mining

  • Anna MonrealeEmail author
  • Dino Pedreschi
  • Ruggero G. Pensa
  • Fabio Pinelli
Article

Abstract

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.

Keywords

Privacy-by-design Sequence data k-anonymity 

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Anna Monreale
    • 1
    • 2
    Email author
  • Dino Pedreschi
    • 1
    • 2
  • Ruggero G. Pensa
    • 3
  • Fabio Pinelli
    • 4
  1. 1.University of PisaPisaItaly
  2. 2.KDD Lab, ISTI-CNRPisaItaly
  3. 3.Department of Computer ScienceUniversity of TorinoTurinItaly
  4. 4.IBM ResearchDublinIreland

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