Authors: Stefanović, TamaraGhilezan, Silvia Affiliations: Mathematics Mathematical Institute of the Serbian Academy of Sciences and Arts Title: Differential Privacy and Applications First page: 52 Last page: 54 Conference: 10th International Conference Logic and Applications, LAP 2021, September 20 - 24, 2020, Dubrovnik, Croatia Issue Date: 2021 Rank: M34 URL: http://imft.ftn.uns.ac.rs/math/cms/uploads/Main/2021_LAP_FORMALS_BoA.pdf Abstract: The right to privacy is considered as a fundamental right. Data privacygenerally concerns whether and how data is shared with a third party, how itis collected and stored, as well as the laws governing data sharing in areas suchas health care, education and financial services [3]. The problem of defining theright to privacy gained special importance with the development of informationtechnology. The first definition of privacy is given in Warren and Brandeis’s 1890seminal book, “The Right to Privacy” [9] and it is inspired by new photographicand printing technologies and their influence on citizens’ personal life. Fromthat moment, new technologies raised new privacy concerns and brought newmeanings of the notion “privacy”. Although technology has developed dataprivacy problems, technology can also help solve them.In order to deal with these problems, we must formalize them first. JeannetteM. Wing highlighted the importance of formal methods in the domain of dataprivacy in [6]. Mathematical formulations of different notions of privacy arehighly important for guiding the development of privacy preserving technologies.One of the best-known mathematical formulations of privacy is DifferentialPrivacy proposed by Cynthia Dwork. The idea is to start with a statisticaldatabase and an adversary who wants to learn some of the sensitive data fromthe database. Differential privacy relies on incorporating random noise so thateverything an adversary receives is noisy and imprecise. Unlike the early pro-posed techniques of anonymization, the differential privacy is not a property ofa database, it is a property of queries, functions applied on a database.Definition 1 [1] Let ε > 0 . A mechanism M is ε-differentially private iff forevery pair of adjacent databases D, D′and for every S ⊆range(M):Pr[M(D) ∈S] ≤exp(ε)Pr[M(D′) ∈S],where the probability space is over the coin flips of the mechanism M.In [4] we have compared different models for privacy preserving. In thispaper we deal in more detail with the concept of differential privacy and it’sapplications. One of the recent applications is differential privacy on graphs [2]implemented in social media and recommendation systems [5]. Another currentapplication is in the domain of location privacy and processing of geolocationdata like [7]. Finally, we discuss the latest ideas for application in the blockchaintechnology [8]. Keywords: Differential Privacy | Privacy on Graphs | Location Privacy | Blockchain Publisher: University Center Dubrovnik, Croatia Project: Advanced artificial intelligence techniques for analysis and design of system components based on trustworthy BlockChain technology - AI4TrustBC

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