Efficient Privacy-Preserving Scheme for Distributed Machine Learning Against Collusion Attacks (EPPS-DMLCA)

Abstract

We propose EPPS-DMLCA, a novel method integrating homomorphic encryption and DP to protect data from attacks. EPPS-DMLCA improves techniques to protect data from attacks in several ways. One, it protects data while being more computationally efficient compared to extant methods. Two, it is more robust to collusion attacks than current methods are. We evaluated EPPS-DXMCA using MNIST. We tested our method on Amazon’s cloud using 40 cloud servers. We found that our method performed as well as a method that did not protect privacy. Additionally, our method uses a firm privacy budget, which provides robust privacy protection. Last, we found that because of the low computational overhead, our method is more resistant to collusion attacks than state-of-the-art methods are. Our results show that EPPS-DMLCA is an important contribution to the literature. We propose EPPS-DMLCA, which integrates homomorphic encryption and DP to protect data from attacks. We used MNIST in a distributed cloud environment with 40 cloud servers. Our results show that EPPS-DMLCA is up to 3 times more efficient than FHE methods. Moreover, the model performs 97.8%, the same as unprotected methods. Last, DP’s noise was well-tuned, as evidenced by the model and the sufficient parameters to provide robust protection from privacy. Moreover, EPPS-DMLCA is more resistant to collusion than the state-of-the-art. Machine learning models are essential for creating information from big data in a constantly changing world. However, access to such big data raises privacy issues. Fortunately, various ways exist to protect data from attacks. One such method is DP, which adds noise to the data. The goal of DP is to generate robust protection from privacy. DP is a valuable tool, but the method has one drawback. The method uses many noise probes to be effective. This means that FHE methods do not perform as well as unprotected methods. Because of this drawback, I propose EPPS-DMLCA to users.