We initiate the study of hypothesis selection under local differential privacy. Given samples from an unknown probability distribution\u00a0\\(p\\)\u00a0and a set of\u00a0\\(k\\)\u00a0probability distributions\u00a0\\(Q\\), we aim to output, under the constraints of\u00a0\\(\\epsilon\\)-local differential privacy, a distribution from\u00a0\\(Q\\)\u00a0whose total variation distance to\u00a0\\(p\\)\u00a0is comparable to the best such distribution.<\/p>\n","protected":false},"excerpt":{"rendered":"
We initiate the study of hypothesis selection under local differential privacy. Given samples from an unknown probability distribution\u00a0\u00a0and a set of\u00a0\u00a0probability distributions\u00a0, we aim to output, under the constraints of\u00a0-local differential privacy, a distribution from\u00a0\u00a0whose total variation distance to\u00a0\u00a0is comparable to the best such distribution.<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"Sivakanth Gopi","user_id":"37830"},{"type":"text","value":"Gautam Kamath","user_id":0},{"type":"user_nicename","value":"Janardhan Kulkarni","user_id":"32147"},{"type":"text","value":"Aleksandar Nikolov","user_id":0},{"type":"text","value":"Zhiwei Steven Wu","user_id":0},{"type":"text","value":"Huanyu 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