Completeness theorems for non-cryptographic fault-tolerant distributed computation
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
Untraceable electronic mail, return addresses, and digital pseudonyms
Communications of the ACM
On securely scheduling a meeting
Sec '01 Proceedings of the 16th international conference on Information security: Trusted information: the new decade challenge
The Effect of Policies for Selecting the Solution of a DisCSP on Privacy Loss
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Asynchronous aggregation and consistency in distributed constraint satisfaction
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Autonomous Agents and Multi-Agent Systems
Experimental analysis of privacy loss in DCOP algorithms
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
SSDPOP: improving the privacy of DCOP with secret sharing
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Distributed Meeting Scheduling
Proceedings of the 2007 conference on Artificial Intelligence Research and Development
Privacy in Distributed Meeting Scheduling
Proceedings of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
Analysis of privacy loss in distributed constraint optimization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Asynchronous aggregation and consistency in distributed constraint satisfaction
Artificial Intelligence - Special issue: Distributed constraint satisfaction
A Cryptographic Solution for Private Distributed Simple Meeting Scheduling
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
A Cryptographic Solution for Private Distributed Simple Meeting Scheduling
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
Privacy-preserving activity scheduling on mobile devices
Proceedings of the first ACM conference on Data and application security and privacy
Meetings through the cloud: Privacy-preserving scheduling on mobile devices
Journal of Systems and Software
Filtering for private collaborative benchmarking
ETRICS'06 Proceedings of the 2006 international conference on Emerging Trends in Information and Communication Security
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Distributed problems raise privacy issues. The user would like to specify securely his constraints (desires, availability, money) on his computer once. The computer is expected to compute and communicate for searching an acceptable solution while maintaining the privacy of the user. Even without computers infested with spy viruses that capture the interaction with the user, most agent based approaches reveal parts of one agent's secret data to its partners in distributed computations [Using privacy loss to guide decisions in distributed CSP search]. Some cryptographic multi-party computation protocols [Completeness theorems for non-cryptographic fault-tolerant distributed computating] succeed to avoid leaking secrets at the computation of some functions with private inputs. They have been applied to find the set of all solutions for the meeting scheduling problem [On securely scheduling a meeting]. However, nobody yet succeeded to apply those techniques for finding a random solution to the meeting scheduling problem. Note that revealing all solutions, when you only need a single one, leaks a lot of data about when others are, or are not, available. Some answers were proposed in our previous approaches to distributed constraint problems [Solving a distributed CSP with cryptographic multi-party computations, without revealing constraints and without involving trusted servers]. They guarantee that no agent can infer with certitudea secret from the identity of the solution of the problem (other than the acceptance of the solution), but guarantee nothing about inference of probabilistic information about secrets. Our new technique answers this problem, too.