Quantifying Privacy Leakage in Multi-Agent Planning. Štolba, M.; Tožička, J.; and Komenda, A. In
Quantifying Privacy Leakage in Multi-Agent Planning [link]Paper  abstract   bibtex   
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.
@INPROCEEDINGS{dmap2016stolba1,
author = {Michal {\v{S}}tolba and Jan To{\v{z}}i{\v{c}}ka and Anton{\'{\i}}n Komenda},
title = {Quantifying Privacy Leakage in Multi-Agent Planning},
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 (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.},
url = {http://icaps16.icaps-conference.org/proceedings/dmap16.pdf#page=83}
}
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