Data Mining with Secure Computation

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ECML PKDD 2017        |       Friday, September 22, 2017

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Security and privacy aspects of data analytics become of central importance in many application areas. New legislation also pushes companies and research communities to address challenges of privacy-preserving data analytics. In our data mining community, questions about data privacy and security have been predominantly approached from the perspective of k-anonymity and differential privacy.

The aim of this workshop is to draw attention to secure multiparty computation (MPC), a subfield of cryptology, as the key foundation for building privacy-preserving data mining (DM) and machine learning (ML). In this approach, sensitive data is typically secret-shared over multiple players, such that those players can jointly perform DM / ML computations, but individual players (or collusions) cannot learn anything about the data, beyond the result of the computations.


The workshop is sponsored by H2020 SODA project, by Cybernetica / Sharemind and by Partisia.