Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Self-Organizing Maps
Privacy Preserving Data Mining
CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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 Bayesian network structure computation on distributed heterogeneous data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
Privacy-preserving distributed k-means clustering over arbitrarily partitioned data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Privacy-preservation for gradient descent methods
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
Privacy-Preserving decision trees over vertically partitioned data
DBSec'05 Proceedings of the 19th annual IFIP WG 11.3 working conference on Data and Applications Security
On private scalar product computation for privacy-preserving data mining
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
Privacy-preserving linear fisher discriminant analysis
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A graph enrichment based clustering over vertically partitioned data
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
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Privacy-preserving data mining seeks to allow the cooperative execution of data mining algorithms while preserving the data privacy of each party concerned. In recent years, many data mining algorithms have been enhanced with privacy-preserving feature: decision tree induction, frequent itemset counting, association analysis, k-means clustering, support vector machine, Naïve Bayes classifier, Bayesian networks, and so on. In this paper, we propose a protocol for privacy-preserving self-organizing map for vertically partitioned data involving two parties. Self-organizing map (SOM) is awidely used algorithmfor transforming data sets to a lower dimensional space to facilitate visualization. The challenges in preserving data privacy in SOM are (1) to securely discover the winner neuron from data privately held by two parties; (2) to securely update weight vectors of neurons; and (3) to securely determine the termination status of SOM. We propose protocols to address the above challenges. We prove that these protocols are correct and privacy-preserving. Also, we prove that the intermediate results generated by these protocols do not violate the data privacy of the participating parties.