Quantifying Privacy Leakage in Multi-Agent Planning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00307284" target="_blank" >RIV/68407700:21230/16:00307284 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Quantifying Privacy Leakage in Multi-Agent Planning

Popis výsledku v původním jazyce

Multi-agent planning using MA-STRIPS-related models is often motivated by the preservation of private information. Such motivation is not only natural for multi-agent systems, but is one of the main reasons, why multi-agent planning (MAP) problems cannot be solved centrally. Although the motivation is common in the literature, formal treatment of privacy is mostly missing. An exception is a definition of two extreme concepts, weak and strong privacy. In this paper, we first analyze privacy leakage in the terms of secure Multi-Party Computation and Quantitative Information Flow. Then, we follow by analyzing privacy leakage of the most common MAP paradigms. Finally, we propose a new theoretical class of secure MAP algorithms and show how the existing techniques can be modified in order to fall in the proposed class.

Název v anglickém jazyce

Quantifying Privacy Leakage in Multi-Agent Planning

Popis výsledku anglicky

Multi-agent planning using MA-STRIPS-related models is often motivated by the preservation of private information. Such motivation is not only natural for multi-agent systems, but is one of the main reasons, why multi-agent planning (MAP) problems cannot be solved centrally. Although the motivation is common in the literature, formal treatment of privacy is mostly missing. An exception is a definition of two extreme concepts, weak and strong privacy. In this paper, we first analyze privacy leakage in the terms of secure Multi-Party Computation and Quantitative Information Flow. Then, we follow by analyzing privacy leakage of the most common MAP paradigms. Finally, we propose a new theoretical class of secure MAP algorithms and show how the existing techniques can be modified in order to fall in the proposed class.

Druh

O - Ostatní výsledky

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

<a href="/cs/project/GJ15-20433Y" target="_blank" >GJ15-20433Y: Heuristické prohledávání pro multiagentní a faktorové plánování</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů