Privacy for Free: Posterior Sampling and Stochastic Gradient Monte CarloEI

    Cited by: 112|Bibtex|25|

    International Conference on Machine Learning, 2015.

    Abstract:

    We consider the problem of Bayesian learning on sensitive datasets and present two simple but somewhat surprising results that connect Bayesian learning to \"differential privacy\", a cryptographic approach to protect individual-level privacy while permitting database-level utility. Specifically, we show that under standard assumptions, g...More
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