A Privacy-Preserving Platform for User-Centric Quantitative Benchmarking
TrustBus '09 Proceedings of the 6th International Conference on Trust, Privacy and Security in Digital Business
An information-flow type-system for mixed protocol secure computation
Proceedings of the 8th ACM SIGSAC symposium on Information, computer and communications security
Efficient secure computation optimization
Proceedings of the First ACM workshop on Language support for privacy-enhancing technologies
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Secure Multiparty Computation (SMC) protocols enable a group of mutually distrustful parties to perform a joint computation with private inputs. Novel e-commerce applications have emerged that could benefit from strong privacy protection, e.g., benchmarking, auctions, and collaborative supply chain management and planning. However, the uptake of SMC in these applications is still rare. We argue that this is due to poor performance, functionality, and scalability, as well as architectures that do not meet the needs of e-commerce applications. This paper explores SMC approaches and research directions, aiming at providing better support for e-commerce applications.