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Dimitrakakis, Christos

Nom
Dimitrakakis, Christos
Affiliation principale
Fonction
Professor
Email
christos.dimitrakakis@unine.ch
Identifiants
https://libra.unine.ch/handle/123456789/30936
_
0000-0002-5367-5189

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  • Publication
    Accès libre
    On The Differential Privacy of Thompson Sampling With Gaussian Prior
    (2018-06-24T18:37:09Z)
    Aristide C. Y. Tossou
    ;
    Dimitrakakis, Christos 
    We show that Thompson Sampling with Gaussian Prior as detailed by Algorithm 2 in (Agrawal & Goyal, 2013) is already differentially private. Theorem 1 show that it enjoys a very competitive privacy loss of only O(ln2T) after T rounds. Finally, Theorem 2 show that one can control the privacy loss to any desirable ϵ level by appropriately increasing the variance of the samples from the Gaussian posterior. And this increases the regret only by a term of O(ln2Tϵ). This compares favorably to the previous result for Thompson Sampling in the literature ((Mishra & Thakurta, 2015)) which adds a term of O(Kln3Tϵ2) to the regret in order to achieve the same privacy level. Furthermore, our result use the basic Thompson Sampling with few modifications whereas the result of (Mishra & Thakurta, 2015) required sophisticated constructions.
  • Publication
    Accès libre
    On the Differential Privacy of Bayesian Inference
    (2015-12-22T09:22:39Z)
    Zuhe Zhang
    ;
    Benjamin Rubinstein
    ;
    Dimitrakakis, Christos 
    We study how to communicate findings of Bayesian inference to third parties, while preserving the strong guarantee of differential privacy. Our main contributions are four different algorithms for private Bayesian inference on proba-bilistic graphical models. These include two mechanisms for adding noise to the Bayesian updates, either directly to the posterior parameters, or to their Fourier transform so as to preserve update consistency. We also utilise a recently introduced posterior sampling mechanism, for which we prove bounds for the specific but general case of discrete Bayesian networks; and we introduce a maximum-a-posteriori private mechanism. Our analysis includes utility and privacy bounds, with a novel focus on the influence of graph structure on privacy. Worked examples and experiments with Bayesian na{\"i}ve Bayes and Bayesian linear regression illustrate the application of our mechanisms.