Privacy Preserving Access Controlled Interactive Clustering as Service Over Hybrid Cloud
Keywords:
Clustering, Deep Learning, Generative Adversarial Network, Privacy preservingAbstract
Mining on large volume of data offloaded by enterprises to cloud has become an integral part in business strategy design. But the mining must be privacy preserving as leakage of data or mined information can create various security and privacy threats. Towards this end, privacy preserving data mining techniques can become critical. This work proposes a privacy preserving clustering model with support of incremental and adaptive clustering, fine grained access control and prevention from leakage of data and security parameters. The clustering is built on privacy preserved locality sensitive hashing technique on binary vector summarized data. The privacy of security parameter is ensured using generative adversarial network (GAN) deep learning model. Through experimental analysis, the proposed solution is found to reduce the computation cost for clustering by 40%, communication cost by 12% and able to provide better clustering accuracy with ARI of about 5% compared to existing works.
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