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M. Atzori, F. Bonchi, F. Giannotti, D. Pedreschi KDD Laboratory University of Pisa and ISTI-CNR

M. Atzori, F. Bonchi, F. Giannotti, D. Pedreschi KDD Laboratory University of Pisa and ISTI-CNR Speaker: Maurizio Atzori GeoPKDD, Venezia 18 October 2005. Pisa KDD Laboratory http://www-kdd.isti.cnr.it/. Anonymity Aware Data Mining. Our Taxonomy of Privacy Preserving Data Mining.

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M. Atzori, F. Bonchi, F. Giannotti, D. Pedreschi KDD Laboratory University of Pisa and ISTI-CNR

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  1. M. Atzori, F. Bonchi, F. Giannotti, D. Pedreschi KDD Laboratory University of Pisa and ISTI-CNR Speaker: Maurizio Atzori GeoPKDD, Venezia 18 October 2005 Pisa KDD Laboratory http://www-kdd.isti.cnr.it/ Anonymity Aware Data Mining

  2. Our Taxonomy of Privacy Preserving Data Mining • Intesional Knowledge Hiding • Bertino’s approach, based on DB Sanitization • Extensional Knowledge Hiding • Agrawal’s approach, based on DB Randomization • Sweeney’s approach, based on DB Anonymization • Distributed Extensional Knowledge Hiding • Clifton’s approach based on Secure Multiparty Computation • Secure Intesional Knowledge Sharing • Clifton’s Public/Private/Unknown Attribute Framework • Zaiane’s Association Rule Sanitization (but also IKH) • Our approach

  3. A Motivating Example • Suppose that The Fair Hospital, by doing association analysis, discovered some interesting patterns in its patient data • Now, such an institution wants to: • publish those research results • NOT release any personal information of its patients (i.e. Information that regard only few patients)

  4. Association Analysis can Break Anonymity • a1  a2  a3  a4 • [sup = 80, conf = 98.7%]

  5. Association Analysis can Break Anonymity sup(a1, a2, a3, a4) conf • a1  a2  a3  a4 • [sup = 80, conf = 98.7%] • sup(a1,a2,a3) = = 81

  6. Association Analysis can Break Anonymity sup(a1, a2, a3, a4) conf • a1  a2  a3  a4 • [sup = 80, conf = 98.7%] • sup(a1,a2,a3) = = 81 • Therefore there is only one individual such that the pattern a1  a2  a3 ¬a4 holds • This information can be used as a quasi-identifier for that person, breaking her anonyimity

  7. Bad and Good Things • BAD THING: In general Data Mining results contain information related to few individuals • This is true even if we use high values for the support threshold (i.e. Forcing samples to be large) • GOOD THING: inference channels leading to anonymity breaches can be discovered and removed

  8. Our Approach: k-Anonymous Patterns • Based on Relational DB K-anonymity [Sweeney, 1998-2002] • We can transform a Relation into another more general s.t. Every row appears at least k times • From Data to Pattern k-anonymity • A set of patterns is k-anonymous if it is impossible to infer anything about a set of individuals of cardinality less than k • DB K-anonymity guarantees the extraction of only k-anonymous patterns, but: • Optimal DB k-anonymity is NP-Complete (very high computational cost) • DB k-anonymity destroys also safe information

  9. Summary: Present and Future • What we did: • Formalization of such anonymity threats in data mining models, based on the concept of Sweeney’s k-Anonymity • We developed an Inference Channel Detector able to discover all anonymity breaches [Atzori et al., PKDD05] • We developed Sanitization Strategies to slightly modify the output of data mining algorithm in order to guarantee [Atzori et al., ICDM05] • What we (hopefully) will do: • Apply k-anonymity framework to Spatio-Temporal Databases and Data Mining

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