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Differentially private Bayesian learning on distributed data
Mikko Heikkilä · Eemil Lagerspetz · Samuel Kaski · Kana Shimizu · Sasu Tarkoma · Antti Honkela

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #189

Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects. Differential privacy (DP) has become established as a standard for protecting learning results. The standard DP algorithms require a single trusted party to have access to the entire data, which is a clear weakness, or add prohibitive amounts of noise. We consider DP Bayesian learning in a distributed setting, where each party only holds a single sample or a few samples of the data. We propose a learning strategy based on a secure multi-party sum function for aggregating summaries from data holders and the Gaussian mechanism for DP. Our method builds on an asymptotically optimal and practically efficient DP Bayesian inference with rapidly diminishing extra cost.

Author Information

Mikko Heikkilä (University of Helsinki)
Eemil Lagerspetz (University of Helsinki)

Dr. Eemil Lagerspetz is a postdoctoral researcher at University of Helsinki. His research interests include ubiquitous computing, large-scale data analysis, energy efficiency, and distributed systems.

Samuel Kaski (Aalto University)
Kana Shimizu (Waseda University)
Sasu Tarkoma (University of Helsinki)
Antti Honkela (University of Helsinki)

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