State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey

Understand

This paper examines the evolving landscape of machine learning (ML) and its profound impact across various sectors, with a special focus on the emerging field of Privacy-preserving Machine Learning (PPML).

• As ML applications become increasingly integral to industries like telecommunications, financial technology, and surveillance, they raise significant privacy concerns, necessitating the development of PPML strategies.
• The paper highlights the unique challenges in safeguarding privacy within ML frameworks, which stem from the diverse capabilities of potential adversaries, including their ability to infer sensitive information from model outputs or training data.
• We delve into the spectrum of threat models that characterize adversarial intentions, ranging from membership and attribute inference to data reconstruction.