Privacy Loss in Distributed Constraint Reasoning: A Quantitative Framework for Analysis and its Applications

@article{Maheswaran2006PrivacyLI,
  title={Privacy Loss in Distributed Constraint Reasoning: A Quantitative Framework for Analysis and its Applications},
  author={Rajiv T. Maheswaran and Jonathan P. Pearce and Emma Bowring and Pradeep Varakantham and Milind Tambe},
  journal={Autonomous Agents and Multi-Agent Systems},
  year={2006},
  volume={13},
  pages={27-60}
}
It is critical that agents deployed in real-world settings, such as businesses, offices, universities and research laboratories, protect their individual users’ privacy when interacting with other entities. Indeed, privacy is recognized as a key motivating factor in the design of several multiagent algorithms, such as in distributed constraint reasoning (including both algorithms for distributed constraint optimization (DCOP) and distributed constraint satisfaction (DisCSPs)), and researchers… CONTINUE READING
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