Secret sharing homomorphisms: keeping shares of a secret secret
Proceedings on Advances in cryptology---CRYPTO '86
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Distributed multivariate regression using wavelet-based collective data mining
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Secure multi-party computation problems and their applications: a review and open problems
Proceedings of the 2001 workshop on New security paradigms
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
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 k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Deriving private information from randomized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Data confidentiality, data quality and data integration for federal databases
dg.o '06 Proceedings of the 2006 international conference on Digital government research
Privacy-preserving regression algorithms
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Compressed and privacy-sensitive sparse regression
IEEE Transactions on Information Theory
Differential privacy with compression
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 4
Privacy-aware regression modeling of participatory sensing data
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Privacy-Preserving SVM classification on vertically partitioned data
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Privacy-preserving subgraph discovery
DBSec'12 Proceedings of the 26th Annual IFIP WG 11.3 conference on Data and Applications Security and Privacy
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Reluctance of data owners to share their possibly confidential or proprietary data with others who own related databases is a serious impediment to conducting a mutually beneficial data mining analysis. We address the case of vertically partitioned data -- multiple data owners/agencies each possess a few attributes of every data record. We focus on the case of the agencies wanting to conduct a linear regression analysis with complete records without disclosing values of their own attributes. This paper describes an algorithm that enables such agencies to compute the exact regression coefficients of the global regression equation and also perform some basic goodness-of-fit diagnostics while protecting the confidentiality of their data. In more general settings beyond the privacy scenario, this algorithm can also be viewed as method for the distributed computation for regression analyses.