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O geometriji diferencirane zasebnosti : delo diplomskega seminarja
ID Lodrant, Luka (Author), ID Peperko, Aljoša (Mentor) More about this mentor... This link opens in a new window

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Abstract
V delu najprej predstavimo pojem diferencirane zasebnosti, kot strogo matematično definicijo zasebnosti podatkov, ki pride do izraza pri njihovi javni objavi. Definiramo splošno okolje za numerične podatke, nato pa ocenimo spodnjo mejo napake, ki jo zaseben odzivni mehanizem mora vnesti v podatke. Predstavimo Laplaceov mehanizem, podrobneje pa še $K$-normni in rekurzivni NIM mehanizem. Za vse izpeljemo tudi zgornjo mejo napake in tako za $K$-normni ter NIM mehanizem ocenimo, da sta na določenih razredih poizvedb asimptotsko optimalna. Mehanizme implementiramo in obravnavamo težave, ki pri tem nastanejo.

Language:Slovenian
Keywords:diferencirana zasebnost, odzivni mehanizem, K-normni mehanizem, izotropski položaj
Work type:Final seminar paper
Organization:FMF - Faculty of Mathematics and Physics
Year:2019
PID:20.500.12556/RUL-110365 This link opens in a new window
UDC:519.2
COBISS.SI-ID:18723161 This link opens in a new window
Publication date in RUL:14.09.2019
Views:849
Downloads:142
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Secondary language

Language:English
Title:On the Geometry of Differential Privacy
Abstract:
In this work we present the concept of differential privacy as a rigorous mathematical definition of privacy, which is required for publishing private data. We define a general setting for numerical data and derive a lower bound for the required error of private mechanisms. Laplace mechanism, $K$-norm mechanism and recursive NIM mechanism are presented, each with an upper bound on its error. We conclude that NIM and $K$-norm mechanism are asimptotically optimal for specific classes of queries. Mechanisms are implemented and problems which arise during the implementation are addressed.

Keywords:differential privacy, response mechanism, K-norm mechanism, isotropic position

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