epsilon-Strong Privacy Preserving Multiagent Planner by Computational Tractability

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00315610" target="_blank" >RIV/68407700:21230/17:00315610 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006176400510057" target="_blank" >http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006176400510057</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0006176400510057" target="_blank" >10.5220/0006176400510057</a>

Jazyk výsledku

angličtina

Název v původním jazyce

epsilon-Strong Privacy Preserving Multiagent Planner by Computational Tractability

Popis výsledku v původním jazyce

Classical planning can solve large and real-world problems, even when multiple entities, such as robots, trucks or companies, are concerned. But when the interested parties, such as cooperating companies, are interested in maintaining their privacy while planning, classical planning cannot be used. Although, privacy is one of the crucial aspects of multi-agent planning, studies of privacy are underepresented in the literature. A strong privacy property, necessary to leak no information at all, has not been achieved by any planner in general yet. In this contribution, we propose a multiagent planner which can get arbitrarily close to the general strong privacy preserving planner for the price of decreased planning efficiency. The strong privacy assurances are under computational tractability assumptions commonly used in secure computation research.

Název v anglickém jazyce

epsilon-Strong Privacy Preserving Multiagent Planner by Computational Tractability

Popis výsledku anglicky

Classical planning can solve large and real-world problems, even when multiple entities, such as robots, trucks or companies, are concerned. But when the interested parties, such as cooperating companies, are interested in maintaining their privacy while planning, classical planning cannot be used. Although, privacy is one of the crucial aspects of multi-agent planning, studies of privacy are underepresented in the literature. A strong privacy property, necessary to leak no information at all, has not been achieved by any planner in general yet. In this contribution, we propose a multiagent planner which can get arbitrarily close to the general strong privacy preserving planner for the price of decreased planning efficiency. The strong privacy assurances are under computational tractability assumptions commonly used in secure computation research.

Druh

D - Stať ve sborníku

CEP obor

—

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 of the 9th International Conference on Agents and Artificial Intelligence

ISBN

978-989-758-219-6

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

51-57

Název nakladatele

SciTePress

Místo vydání

Madeira

Místo konání akce

Porto

Datum konání akce

24. 2. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000413243500004