Secure Multi-Agent Planning

Štolba, Michal; Tožička, Jan; Komenda, Antonín

doi:10.1145/2970030.2970042

Cited by 13 publications

(7 citation statements)

References 7 publications

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“…2) Due to the reduction in messages, it may leak less private information. Fewer messages sent are likely to lead to less private information leakage ( Štolba, Tožička, and Komenda 2016). 3) MAFBS adapts the more refined privacy model of (Bonisoli et al 2014) supporting agent privacy.…”

Section: Discussionmentioning

confidence: 99%

Increased Privacy with Reduced Communication in Multi-Agent Planning

Maliah

Brafman

Shani

2017

ICAPS

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Multi-agent forward search (MAFS) is a state-of-the-art privacy-preserving planning algorithm. We describe a new variant of MAFS, called multi-agent forward-backward search (MAFBS) that uses both forward and backward messages to reduce the number of messages sent and obtain new privacy properties. While MAFS requires agents to send a state s produced by an action a to all agents that can apply any action in s, MAFBS sends such messages forward only to agents that have an action that requires one of the effects of a. To achieve completeness, it sends messages backward to agents that can supply a missing precondition. This more focused message passing scheme reduces states exchanged, and requires that agents be aware only of other agents that they directly interact with, leading to agent privacy.

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Section: Discussionmentioning

confidence: 99%

Increased Privacy with Reduced Communication in Multi-Agent Planning

Maliah

Brafman

Shani

2017

ICAPS

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“…Stolba et al [15], [16], [17] refine privacy metrics by quantifying the amount of privacy loss. In this case, their analysis of privacy loss is conducted by assessing information leakage [18], [19].…”

Section: B Strong Privacy-preserving Approachesmentioning

confidence: 99%

Differentially Private Multi-Agent Planning for Logistic-like Problems

Zhu

Shen

et al. 2020

Preprint

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“…Planning algorithms for privacy preserving can be divided into weak or strong privacy [17], ε-strong privacy [25], and provable guarantees privacy [26].…”

Section: Definition 2 (Model Privacy)mentioning

confidence: 99%

“…In fact, privacy preservation is the goal pursued by multi-agent planning, which has been a crucial concern for multi-agent systems in some contexts, such as agent negotiation [12], multi-agent reinforcement learning and policy iteration [4,5], deep learning [13], and distributed constraint optimization problems (DCOPs) [14][15][16]. Multi-agent planning (MAP) in cooperative environments aims at generating a sequence of actions to fulfill some specified goals [17]. Most multi-agent systems rely intrinsically on collaboration among agents to accomplish a joint task, in which the collaboration depends on the exchange of information among them, so the privacy preservation of the information naturally rises.…”

Section: Introductionmentioning

confidence: 99%

Opponent-Aware Planning with Admissible Privacy Preserving for UGV Security Patrol under Contested Environment

et al. 2019

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Unmanned ground vehicles (UGVs) have been widely used in security patrol. The existence of two potential opponents, the malicious teammate (cooperative) and the hostile observer (adversarial), highlights the importance of privacy-preserving planning under contested environments. In a cooperative setting, the disclosure of private information can be restricted to the malicious teammates. In adversarial setting, obfuscation can be added to control the observability of the adversarial observer. In this paper, we attempt to generate opponent-aware privacy-preserving plans, mainly focusing on two questions: what is opponent-aware privacy-preserving planning, and, how can we generate opponent-aware privacy-preserving plans? We first define the opponent-aware privacy-preserving planning problem, where the generated plans preserve admissible privacy. Then, we demonstrate how to generate opponent-aware privacy-preserving plans. The search-based planning algorithms were restricted to public information shared among the cooperators. The observation of the adversarial observer could be purposefully controlled by exploiting decoy goals and diverse paths. Finally, we model the security patrol problem, where the UGV restricts information sharing and attempts to obfuscate the goal. The simulation experiments with privacy leakage analysis and an indoor robot demonstration show the applicability of our proposed approaches.

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Secure Multi-Agent Planning

Cited by 13 publications

References 7 publications

Increased Privacy with Reduced Communication in Multi-Agent Planning

Increased Privacy with Reduced Communication in Multi-Agent Planning

Differentially Private Multi-Agent Planning for Logistic-like Problems

Opponent-Aware Planning with Admissible Privacy Preserving for UGV Security Patrol under Contested Environment

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