On the Differential Privacy of Bayesian Inference

Zhang, Zuhe (University of Melbourne) | Rubinstein, Benjamin I. P. (University of Melbourne) | Dimitrakakis, Christos (Univ-Lille-3 and Chalmers University of Technology)

AAAI Conferences 

The latter achieves While B wants to learn as much as possible from the data, stealth through consistent posterior updates. For general she doesn't want A to learn about any individual datum. Bayesian networks, posteriors may be nonparametric. In This is for example the case where A is an insurance agency, this case, we explore a mechanism (Dimitrakakis et al. 2014) the data are medical records, and B wants to convey the efficacy which samples from the posterior to answer queries--no additional of drugs to the agency, without revealing the specific noise is injected. We complement our study with illnesses of individuals in the population. Such requirements a maximum a posteriori estimator that leverages the exponential of privacy are of growing interest in the learning (Chaudhuri mechanism (McSherry and Talwar 2007). Our utility and Hsu 2012; Duchi, Jordan, and Wainwright 2013), theoretical and privacy bounds connect privacy and graph/dependency computer science (Dwork and Smith 2009; McSherry structure, and are complemented by illustrative experiments and Talwar 2007) and databases communities (Barak et al. with Bayesian naïve Bayes and linear regression.

Duplicate Docs Excel Report

None found

Similar Docs  Excel Report  more

None found