Nearest Neighbor Tour Circuit Encryption Algorithm Based Random Isomap Reduction
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Accurate and large-scale privacy-preserving data mining using the election paradigm
Data & Knowledge Engineering
Efficient mining of skyline objects in subspaces over data streams
Knowledge and Information Systems
Anonymous biometric access control
EURASIP Journal on Information Security - Special issue on enhancing privacy protection in multimedia systems
Privacy-preserving outsourcing support vector machines with random transformation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving neural networks in iris signature feature extraction
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
Cloud-enabled privacy-preserving collaborative learning for mobile sensing
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
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Traditional Data Mining and Knowledge Discovery algorithms assume free access to data, either at a centralized location or in federated form. Increasingly, privacy and security concerns restrict this access, thus derailing data mining projects. What is required is distributed knowledge discovery that is sensitive to this problem. The key is to obtain valid results, while providing guarantees on the nondisclosure of data. Support vector machine classification is one of the most widely used classification methodologies in data mining and machine learning. It is based on solid theoretical foundations and has wide practical application. This paper proposes a privacy-preserving solution for support vector machine (SVM) classification, PP-SVM for short. Our solution constructs the global SVM classification model from data distributed at multiple parties, without disclosing the data of each party to others. Solutions are sketched out for data that is vertically, horizontally, or even arbitrarily partitioned. We quantify the security and efficiency of the proposed method, and highlight future challenges.