BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Laboratoire de Mathématiques de Versailles - ECPv6.3.5//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Laboratoire de Mathématiques de Versailles X-ORIGINAL-URL:https://lmv.math.cnrs.fr X-WR-CALDESC:évènements pour Laboratoire de Mathématiques de Versailles REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:Europe/Paris BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20200329T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20201025T010000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=Europe/Paris:20200128T113000 DTEND;TZID=Europe/Paris:20200128T123000 DTSTAMP:20240329T002407 CREATED:20191108T155208Z LAST-MODIFIED:20200131T084946Z UID:6848-1580211000-1580214600@lmv.math.cnrs.fr SUMMARY:PS : Zacharie Naulet (Univ. Paris Saclay) : Optimal disclosure risk assessment DESCRIPTION:Protection against disclosure is a legal and ethical obligation for agencies releasing microdata files for public use. Any decision about releasing data  is supported by the estimation of measures of disclosure risk. The most common measure is arguably the number $\tau_{1}$ of sample unique records that are population uniques. We first study nonparametric estimation of $\tau_{1}$. We introduce a class of linear estimators of $\tau_{1}$ that are simple\, computationally efficient and scalable to massive datasets\, and we give uniform theoretical guarantees for them. We then establish a lower bound for the minimax NMSE for the estimation of $\tau_{1}$. \nPS : Zacharie Naulet (Univ. Paris Saclay) : Optimal disclosure risk assessment URL:https://lmv.math.cnrs.fr/evenenement/ps-zacharie-naulet-univ-paris-saclay-titre-a-venir/ LOCATION:Bâtiment Fermat\, salle 2107 CATEGORIES:Séminaire PS END:VEVENT END:VCALENDAR