A Novel Graph Clustering Algorithm with Enhanced Security using Power Method and Homomorphic Encryption

In recent years, privacy concerns have led to an increasing demand for secure data clustering algorithms that can protect sensitive data while maintaining the accuracy of the clustering results. By using homomorphic encryption to enable safe computation on encrypted data is one interesting strategy. Homomorphic encryption approach that allows addition and multiplication to be performed on encrypted data is the Paillier cryptosystem. In this study, we present an innovative approach for using the power iteration algorithm for clustering with the Paillier cryptosystem. An effective and scalable method for grouping large data sets is the power iteration clustering algorithm. We show that by using the Paillier cryptosystem, we can perform the necessary computations for the power iteration clustering algorithm on encrypted data without revealing any sensitive information. We prove our approach can achieve excellent accuracy while maintaining data privacy by evaluating it on many benchmark datasets. Our results show that the Paillier cryptosystem can be a viable solution for privacypreserving clustering applications.