Unlocking the Future: Privacy-Preserving ML Experimentation

Abstract

Abstract: Experiments with machine learning (ML) have become a key source of new ideas in many fields. However, growing worries about data privacy have made it clear that we need ML testing methods that protect privacy. There are new technologies in this piece that let you play around with machine learning without putting your data at risk. Differential privacy, secure multiparty computation (SMPC), homomorphic encryption, federated learning, trusted execution environments (TEEs), making fake data, and using temporary and nameless IDs are some of these technologies. By using these privacy-protecting solutions, businesses can utilize the full potential of machine learning experiments while protecting individuals' privacy rights and staying in line with strict rules.