Investigative Data Mining for Security and Criminal Detection
Investigative Data Mining for Security and Criminal Detection
Building decision tree classifier on private data
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Privacy-Preserving Cooperative Statistical Analysis
ACSAC '01 Proceedings of the 17th Annual Computer Security Applications Conference
Privacy preserving regression modelling via distributed computation
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-Preserving Outlier Detection
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Privately computing a distributed k-nn classifier
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Regression on distributed databases via secure multi-party computation
dg.o '04 Proceedings of the 2004 annual national conference on Digital government research
Privacy Preserving Nearest Neighbor Search
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
The privacy of k-NN retrieval for horizontal partitioned data: new methods and applications
ADC '07 Proceedings of the eighteenth conference on Australasian database - Volume 63
A new efficient privacy-preserving scalar product protocol
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Secure multi party computation algorithm based on infinite product series
WSEAS Transactions on Information Science and Applications
| Hi-index | 0.00 |
Regression is arguably the most applied data analysis method. Today there are many scenarios where data for attributes that correspond to predictor variables and the response variable itself are distributed among several parties that do not trust each other. Privacy-preserving data mining has grown rapidly studying the scenarios where data is vertically partitioned. While algorithms have been developed for many tasks (like clustering, association-rule mining and classification), for regression, the case of only two parties remains open. Also open is the most interesting case when the response variable is to be kept private. This paper provides the first set of algorithms that solves these cases. Our algorithms are practical and only require a commodity server (a supplier of random values) that does not collude with the parties. Our protocols are secure in the spirit of the the semi-honest model.