2026-07-23T20:10:33Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:21286 2024-10-28T09:51:04Z ddc:004 open_access

How Can Algorithms Help in Protecting Our Privacy (Invited Talk) Henzinger, Monika Matching problems Decisions are increasingly automated using rules that were learnt from personal data. Thus, it is important to guarantee that the privacy of the data is protected during the learning process. To formalize the notion of an algorithm that protects the privacy of its data, differential privacy was introduced by Dwork, McSherry, Nissim, and Smith in 2006. It is a rigorous mathematical definition to analyze the privacy properties of an algorithm - or the lack thereof. In this talk I will give an introduction to differential privacy with an emphasis on differential private algorithms that can handle dynamically changing input data. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Monika Henzinger 2024 Is Part Of LIPIcs, Volume 320, 32nd International Symposium on Graph Drawing and Network Visualization (GD 2024) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GD.2024.2 urn:nbn:de:0030-drops-212864 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GD.2024.2 eng https://creativecommons.org/licenses/by/4.0/legalcode