The limits of strong privacy preserving multi-agent planning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00315598" target="_blank" >RIV/68407700:21230/17:00315598 - isvavai.cz</a>

Výsledek na webu

<a href="https://aaai.org/ocs/index.php/ICAPS/ICAPS17/paper/view/15754" target="_blank" >https://aaai.org/ocs/index.php/ICAPS/ICAPS17/paper/view/15754</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

The limits of strong privacy preserving 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 it is one of the main reasons, why multi-agent planning (MAP) problems cannot be solved centrally. In this paper, we analyze privacy-preserving multi-agent planning (PP-MAP) from the perspective of secure multiparty computation (MPC). We discuss the concept of strong privacy and its implications and present two variants of a novel planner, provably strong privacy-preserving in general. As the main contribution, we formulate the limits of strong privacy-preserving planning in the terms of privacy, completeness and efficiency and show that, for a wide class of planning algorithms, all three properties are not achievable at once. Moreover, we provide a restricted variant of strong privacy based on equivalence classes of planning problems and show that an efficient, complete and strong privacy-preserving planner exists for such restriction.

Název v anglickém jazyce

The limits of strong privacy preserving 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 it is one of the main reasons, why multi-agent planning (MAP) problems cannot be solved centrally. In this paper, we analyze privacy-preserving multi-agent planning (PP-MAP) from the perspective of secure multiparty computation (MPC). We discuss the concept of strong privacy and its implications and present two variants of a novel planner, provably strong privacy-preserving in general. As the main contribution, we formulate the limits of strong privacy-preserving planning in the terms of privacy, completeness and efficiency and show that, for a wide class of planning algorithms, all three properties are not achievable at once. Moreover, we provide a restricted variant of strong privacy based on equivalence classes of planning problems and show that an efficient, complete and strong privacy-preserving planner exists for such restriction.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings International Conference on Automated Planning and Scheduling, ICAPS

ISBN

978-1-57735-789-6

ISSN

2334-0835

e-ISSN

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Počet stran výsledku

9

Strana od-do

297-305

Název nakladatele

Association for the Advancement of Artificial Intelligence (AAAI)

Místo vydání

Palo Alto, California

Místo konání akce

Pittsburgh

Datum konání akce

18. 6. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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