STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Average reward reinforcement learning: foundations, algorithms, and empirical results
Machine Learning - Special issue on reinforcement learning
Multi party computations: past and present
PODC '97 Proceedings of the sixteenth annual ACM symposium on Principles of distributed computing
Bayesian learning in negotiation
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
An Internet-based negotiation server for e-commerce
The VLDB Journal — The International Journal on Very Large Data Bases
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-Preserving Cooperative Statistical Analysis
ACSAC '01 Proceedings of the 17th Annual Computer Security Applications Conference
To buy or not to buy: mining airfare data to minimize ticket purchase price
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving reinforcement learning
Proceedings of the 25th international conference on Machine learning
Modeling and negotiating service quality
Service research challenges and solutions for the future internet
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Machine learning techniques are widely used in negotiation systems. To get more accurate and satisfactory learning results, negotiation parties have the desire to employ learning techniques on the union of their past negotiation records. However, negotiation records are usually confidential and private, and owners may not want to reveal the details of these records. In this paper, we introduce a privacy preserving negotiation learning scheme that incorporate secure multiparty computation techniques into negotiation learning algorithms to allow negotiation parties to securely complete the learning process on a union of distributed data sets. As an example, a detailed solution for secure negotiation Q-learning is presented based on two secure multiparty computations: weighted mean and maximum. We also introduce a novel protocol for the secure maximum operation.