Analysis on Privacy Preserving Clustering Methods for Big Datasets Using Random Number Generators
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Abstract
Due to the growing requirement for powerful processing capabilities, safeguarding and processing sensitive data in big data systems has become an increasingly prevalent issue. Additional effort is required to secure sensitive data on a system with numerous connections, each of which has its own privacy policy, and to exchange cryptographic keys in accordance with the appropriate protocols for each of these parties. Data encrypted using homomorphic encryption algorithms can undergo the same kinds of arithmetic operations as data encrypted using plaintext. This allows for the outsourcing of computations to cloud services while also providing data privacy by processing in the encrypted domain. Also, key exchange sessions become easier for everyone involved because of this. This study presents new privacy-preserving clustering algorithms and homomorphic encryption systems that may together operate on a shared HPC infrastructure, like the cloud. Therefore, it is not necessary for the participants in this system to have a lot of processing power, as the system would distribute the most power-intensive jobs to any provider of cloud computing. Multiple clustering techniques can have their distance matrices computed in a way that does not compromise user privacy using our technology.
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