Preventing Privacy Leakage in Cloud Service with Weighted Majority Algorithm in Presonal Information

The article discusses the use of the weighted probabilistic C-Means (WPCM) System, a soft clustering technique that is commonly used for cloud-based data analytics. However, it highlights the risk of exposing sensitive information through raw data when transmitting it to the cloud for analysis. Therefore, the authors propose a modification to the WPCM algorithm that utilizes the BGV encryption system to secure the raw data during the clustering process. The BGV encryption system is a homomorphic encryption method that allows computations to be performed on encrypted data without requiring decryption. This is useful because it preserves the privacy of the data and avoids the need to decrypt it, which can be a time-consuming process. To implement the modified algorithm, the authors use polynomial approximations of the weight value calculations, membership matrix updates, and cluster center updates. The polynomial functions only involve addition and multiplication, which makes them computationally efficient. The authors use the Taylor theorem to ensure that the approximations are accurate and do not affect the overall performance of the algorithm. The effectiveness of the proposed technique is evaluated by comparing it with the standard WPCM algorithm on two large datasets (eGSAD and sWSN). The results show that the proposed method outperforms the standard method in terms of effectiveness, efficiency, and scalability. Specifically, the proposed algorithm achieves better clustering accuracy, requires less computation time, and can handle larger datasets compared to the standard algorithm.