BlockSecML: A Privacy-Preserving Machine Learning Framework Using Homomorphic Encryption, Advanced Encryption Standard-256 (AES-256), and Blockchain
With machine learning being used more and more in sensitive areas like healthcare and finance, protecting the privacy of training data has become a serious concern. Most existing approaches handle encryption, secure computation, and audit logging as separate steps, which creates gaps where data can be exposed. In this...
Deepti Sharda, Deeksha Gupta, Navdeep Kaur et al.
· Cureus Journal of Computer S... · 0 citations