DATA MINING WITH SECURE COMPUTATION
ECML PKDD 2017 | Friday, September 22, 2017
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 and by Sharemind.