2013 Poster Sessions : Privacy-Preserving Ridge Regression on Hundreds of Millions of Records

Student Name : Valeria Nikolaenko
Advisor : Dan Boneh
Research Areas: Computer Systems
Abstract:
Ridge regression is an algorithm that takes as input a large number of data points and finds the best-fit linear curve through these points. The algorithm is a building block for many machine-learning operations. We present a system for privacy-preserving ridge regression. The system outputs the best-fit curve in the clear, but exposes no other information about the input data. Our approach combines two techniques for computing on encrypted data: homomorphic encryption and Yao garbled circuits. We implement the complete system and experiment with it on real data-sets, and show that it is both efficient and practical.

Bio:
Valeria Nikolaenko is a PhD student in Computer Science advised by Prof. Boneh. Her research focuses on computations on encrypted data. Her recent work on privacy preserving data mining algorithms was carried in collaboration with Technicolor research lab.