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Secure Multi-Agent Planning

Authors:

Michal Štolba,

Jan Tožička,

Antonín KomendaAuthors Info & Claims

PrAISe '16: Proceedings of the 1st International Workshop on AI for Privacy and Security

Article No.: 11, Pages 1 - 8

https://doi.org/10.1145/2970030.2970042

Published: 29 August 2016 Publication History

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Abstract

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 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.

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Cited By

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Gerevini ALipovetzky NPercassi FSaetti ASerina I(2023)Width-Based Search for Multi Agent Privacy-Preserving PlanningArtificial Intelligence10.1016/j.artint.2023.103883(103883)Online publication date: Feb-2023
https://doi.org/10.1016/j.artint.2023.103883
Lev Lehman RShani GStern R(2022)Reducing disclosed dependencies in privacy preserving planningAutonomous Agents and Multi-Agent Systems10.1007/s10458-022-09581-736:2Online publication date: 10-Oct-2022
https://doi.org/10.1007/s10458-022-09581-7
Štolba MUrbanovská MKomenda A(2022)Privacy leakage of search-based multi-agent planning algorithmsAutonomous Agents and Multi-Agent Systems10.1007/s10458-022-09568-436:2Online publication date: 2-Jul-2022
https://doi.org/10.1007/s10458-022-09568-4
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Published In

PrAISe '16: Proceedings of the 1st International Workshop on AI for Privacy and Security

August 2016

91 pages

ISBN:9781450343046

DOI:10.1145/2970030

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 August 2016

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Refereed limited

Funding Sources

Grantová Agentura České Republiky
Czech Science Foundation
Grant Agency of the CTU in Prague

Conference

PrAISe '16

PrAISe '16: International Workshop on AI for Privacy and Security

August 29 - 30, 2016

The Hague, Netherlands

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Cited By

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Gerevini ALipovetzky NPercassi FSaetti ASerina I(2023)Width-Based Search for Multi Agent Privacy-Preserving PlanningArtificial Intelligence10.1016/j.artint.2023.103883(103883)Online publication date: Feb-2023
https://doi.org/10.1016/j.artint.2023.103883
Lev Lehman RShani GStern R(2022)Reducing disclosed dependencies in privacy preserving planningAutonomous Agents and Multi-Agent Systems10.1007/s10458-022-09581-736:2Online publication date: 10-Oct-2022
https://doi.org/10.1007/s10458-022-09581-7
Štolba MUrbanovská MKomenda A(2022)Privacy leakage of search-based multi-agent planning algorithmsAutonomous Agents and Multi-Agent Systems10.1007/s10458-022-09568-436:2Online publication date: 2-Jul-2022
https://doi.org/10.1007/s10458-022-09568-4
Hefner TShani GStern R(2022)Privacy preserving planning in multi-agent stochastic environmentsAutonomous Agents and Multi-Agent Systems10.1007/s10458-022-09554-w36:1Online publication date: 26-Mar-2022
https://doi.org/10.1007/s10458-022-09554-w
Moreira LRalha C(2022)An efficient lightweight coordination model to multi-agent planningKnowledge and Information Systems10.1007/s10115-021-01638-5Online publication date: 5-Jan-2022
https://doi.org/10.1007/s10115-021-01638-5
Ye DZhu TShen SZhou WYu P(2020)Differentially Private Multi-Agent Planning for Logistic-like ProblemsIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2020.3017497(1-1)Online publication date: 2020
https://doi.org/10.1109/TDSC.2020.3017497
Luo JZhang WGao WLiao ZJi XGu X(2019)Opponent-Aware Planning with Admissible Privacy Preserving for UGV Security Patrol under Contested EnvironmentElectronics10.3390/electronics90100059:1(5)Online publication date: 18-Dec-2019
https://doi.org/10.3390/electronics9010005
Štolba MTožička JKomenda A(2018)Quantifying Privacy Leakage in Multi-Agent PlanningACM Transactions on Internet Technology10.1145/313332618:3(1-21)Online publication date: 5-Feb-2018
https://dl.acm.org/doi/10.1145/3133326
Komenda ATožička JŠtolba M(2018)$${\upvarepsilon }$$-Strong Privacy Preserving Multi-agent PlanningAgents and Artificial Intelligence10.1007/978-3-319-93581-2_8(137-156)Online publication date: 21-Jun-2018
https://doi.org/10.1007/978-3-319-93581-2_8
Štolba MTožička JKomenda A(2016)Secure multi-agent planning algorithmsProceedings of the Twenty-second European Conference on Artificial Intelligence10.3233/978-1-61499-672-9-1714(1714-1715)Online publication date: 29-Aug-2016
https://dl.acm.org/doi/10.3233/978-1-61499-672-9-1714

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