A Hybrid Multi-Client Filter Based Feature Clustering and Privacy Preserving Classification Framework on High Dimensional Databases
Keywords:
privacy preserving, multi-client privacy preserving model, ensemble classification and clusteringAbstract
A multi-client perturbation based data clustering approach for privacy preserving multi-client data analysis is one of the best strategy for the multi-client data privacy applications. The approach is based on the concept of adding noise to the data, in order to make it difficult for an attacker to infer sensitive information about individual data points, while still allowing for meaningful analysis to be performed. The data is partitioned among multiple clients and each client applies a local clustering algorithm to their data. The clients then share their local clustering results with each other, but not the actual data. A global clustering is then constructed by combining the local clustering results.In addition to this, it proposes an optimal bayesian privacy preserving approach using advanced CP-ABE scheme. This approach uses the concept of ciphertext-policy attribute-based encryption (CP-ABE) to encrypt the data and to provide fine-grained access control to the data. The approach estimates the joint probability of the data across multiple clients and uses this estimation to calculate the Bayes Score, which is a measure of the accuracy of the classifier. By maximizing the Bayes Score, the method can select the optimal classifier for the multi-client data while preserving the privacy of the individual clients.Experimental results on different datasets demonstrate that the proposed approach achieves good clustering performance while preserving the privacy of the individual data points. The results also show that the proposed optimal bayesian privacy preserving approach using advanced CP-ABE scheme can effectively protect the privacy of the data while providing accurate results.
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