ACM Computing Surveys (CSUR)
Multidimensional binary search trees used for associative searching
Communications of the ACM
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Private collaborative forecasting and benchmarking
Proceedings of the 2004 ACM workshop on Privacy in the electronic society
Business-to-business electronic market place selection
Enterprise Information Systems
Electronic marketplace definition and classification: literature review and clarifications
Enterprise Information Systems
Filtering for private collaborative benchmarking
ETRICS'06 Proceedings of the 2006 international conference on Emerging Trends in Information and Communication Security
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
Improved primitives for secure multiparty integer computation
SCN'10 Proceedings of the 7th international conference on Security and cryptography for networks
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Benchmarking is the process of comparing one's own performance to the statistics of a group of competitors, named peer group. It is a common and important process in the business world for many important business metrics, called key performance indicators (KPI). Privacy is of the utmost importance, since these KPIs allow the inference of sensitive information. Therefore several secure multi-party computation protocols for securely and privately computing the statistics of KPIs have recently been developed. These protocols are the basic building blocks for a privacy-preserving benchmarking system, but in order to complete an enterprise system that offers a benchmarking service to its customers more problems need to be solved. We first analyse how peer group participation impacts privacy and vice versa. Peer group formation is the process of forming sensible peer groups out of the set of subscribers. We characterise subscribers by a set of discrete criteria and therefore view the automatic peer group formation as a data clustering problem. We present a high-performance modification of k-means clustering that takes the minimum cluster size as an additional parameter which might be of independent interest. Our final approach is the first automatic peer group formation algorithm for an enterprise benchmarking system.