{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:39:37Z","timestamp":1723016377791},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p>Many applications of Bayesian data analysis involve sensitive information such as personal documents or medical records, motivating methods which ensure that privacy is protected. We introduce a general privacy-preserving framework for Variational Bayes (VB), a widely used optimization-based Bayesian inference method. Our framework respects differential privacy, the gold-standard privacy criterion. The iterative nature of variational Bayes presents a challenge since iterations increase the amount of noise needed to ensure privacy. We overcome this by combining: (1) an improved composition method, called the moments accountant, and (2) the privacy amplification effect of subsampling mini-batches from large-scale data in stochastic learning.   We empirically demonstrate the effectiveness of our method on LDA topic models, evaluated on Wikipedia.  In the full paper we extend our method to a broad class of models, including Bayesian logistic regression and sigmoid belief networks.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/705","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T12:12:10Z","timestamp":1594210330000},"page":"5050-5054","source":"Crossref","is-referenced-by-count":0,"title":["Variational Bayes in Private Settings (VIPS) (Extended Abstract)"],"prefix":"10.24963","author":[{"given":"James R.","family":"Foulds","sequence":"first","affiliation":[{"name":"Department of Information Systems, University of Maryland, Baltimore County"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mijung","family":"Park","sequence":"additional","affiliation":[{"name":"Max Planck Institute for Intelligent Systems"},{"name":"Department of Computer Science, University of Tubingen"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kamalika","family":"Chaudhuri","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of California, San Diego"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Max","family":"Welling","sequence":"additional","affiliation":[{"name":"Amsterdam Machine Learning LAB (AMLAB), Informatics Institute, University of Amsterdam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-PRICAI-2020","name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","start":{"date-parts":[[2020,7,11]]},"theme":"Artificial Intelligence","location":"Yokohama, Japan","end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T02:16:46Z","timestamp":1594261006000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/705"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/705","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}