Completeness theorems for non-cryptographic fault-tolerant distributed computation
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Towards a universal test suite for combinatorial auction algorithms
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On securely scheduling a meeting
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Asynchronous Search with Aggregations
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Asynchronous Weak-commitment Search for Solving Distributed Constraint Satisfaction Problems
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
Secure Distributed Constraint Satisfaction: Reaching Agreement without Revealing Private Information
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
On the Security of ElGamal Based Encryption
PKC '98 Proceedings of the First International Workshop on Practice and Theory in Public Key Cryptography: Public Key Cryptography
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Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling
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Fully homomorphic encryption using ideal lattices
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Asynchronous Forward-Bounding for Distributed Constraints Optimization
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Secure distributed constraint satisfaction: reaching agreement without revealing private information
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Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
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Artificial Intelligence - Special issue: Distributed constraint satisfaction
A threshold cryptosystem without a trusted party
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Distributed forward checking may lie for privacy
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BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm
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Privacy-preserving activity scheduling on mobile devices
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IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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As large-scale theft of data from corporate servers is becoming increasingly common, it becomes interesting to examine alternatives to the paradigm of centralizing sensitive data into large databases. Instead, one could use cryptography and distributed computation so that sensitive data can be supplied and processed in encrypted form, and only the final result is made known. In this paper, we examine how such a paradigm can be used to implement constraint satisfaction, a technique that can solve a broad class of AI problems such as resource allocation, planning, scheduling, and diagnosis. Most previous work on privacy in constraint satisfaction only attempted to protect specific types of information, in particular the feasibility of particular combinations of decisions. We formalize and extend these restricted notions of privacy by introducing four types of private information, including the feasibility of decisions and the final decisions made, but also the identities of the participants and the topology of the problem. We present distributed algorithms that allow computing solutions to constraint satisfaction problems while maintaining these four types of privacy. We formally prove the privacy properties of these algorithms, and show experiments that compare their respective performance on benchmark problems.