Data Privacy in Machine Learning: Balancing Efficiency with Ethical Considerations

Abstract

When it comes to making predictions and judgements using massive amounts of data, machine learning (ML) has been a game-changer for a number of sectors. Data privacy and the ethical consequences of data usage are major challenges that are brought up by the increasing dependence on data-driven models. there is a conflict between the need for machine learning algorithms to access large volumes of sensitive data in order to function efficiently and the ethical concerns related to data protection, permission, and privacy. We look at the problems with data anonymisation, the danger of re-identification, and the compromises between privacy and model accuracy that arise while training ML models. takes a look at ML privacy-preserving methods including homomorphic encryption, federated learning, and differential privacy that try to safeguard people's privacy without lowering ML models' performance. We also go over how legal frameworks like the General Data Protection Regulation (GDPR) may help establish norms for ethical data processing and guarantee that users' privacy is protected at every stage of machine learning. Keeping trust and transparency in machine learning's use in mind, this paper offers insights on how the technology might progress responsibly while balancing data availability for innovation and the imperative of safeguarding personal information.