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Privacy preserving data mining via homomorphic encryption

Hold Date
2017-05-26 15:00〜2017-05-26 16:00
Seminar Room W1-C-716, West Zone 1, Ito campus, Kyushu University
Object person
Takuya Hayashi (Kobe Univ./NICT)

Privacy preserving data mining (PPDM) is crucially important technology for balancing the data privacy and the data utility. One strategy for realizing the PPDM is utilizing homomorphic encryption (HE), which enables us to compute some operations on encrypted data. However, current HEs have several issues, e.g., large computational resource requirement and limitation of computable operations. In this talk, firstly we will introduce our results of HE: security updatable homomorphic encryption and efficient secure inner product computation, then introduce how to utilize HE to compute logistic regression efficiently and securely.
This is a joint work with my colleagues at NICT: Yoshinori Aono, Le Trieu Phong, and Lihua Wang.