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DTSTART;TZID=Europe/Helsinki:20220328T140000
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SUMMARY:Julien CHHOR et Flore SENTENAC (ENSAE-CREST) - "Robust estimation of discrete distributions under local differential privacy "
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:15 pm\nDate: 28th of March 2022\nPlace: Amphi 200 \nJulien CHHOR et Flore SENTENAC (ENSAE-CREST) – “Robust estimation of discrete distributions under local differential privacy” \nAbstract: Although robust learning and local differential privacy are both widely studied fields of research\, combining the two settings is an almost unexplored topic. We consider the problem of estimating a discrete distribution in total variation from n contaminated data batches under a local differential privacy constraint. A fraction 1−ϵ of the batches contain k i.i.d. samples drawn from a discrete distribution p over d elements. To protect the users’ privacy\, each of the samples is privatized using an α-locally differentially private mechanism. \n The remaining ϵn batches are an adversarial contamination. The minimax rate of estimation under contamination alone\, with no privacy\, is known\, up to a √ log(1/ϵ) factor. Under the privacy constraint alone\, the minimax rate of estimation is also known. We characterize the minimax estimation rate under the two constraints up to a √ log(1/ϵ) factor\, which is larger than the sum of the two separate rates. We provide a polynomial-time algorithm achieving this bound\, as well as a matching information theoretic lower bound. \n  \nOrganizers:\nCristina BUTUCEA (CREST)\, Alexandre TSYBAKOV (CREST)\, Karim LOUNICI (CMAP) \, Jaouad MOURTADA (CREST)\nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/julien-chhor-et-flore-sentenac-ensae-crest-robust-estimation-of-discrete-distributions-under-local-differential-privacy/
CATEGORIES:Statistics
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