State of the Art in Zero-Knowledge Machine Learning: A Comprehensive Survey

In recent years, the field of Machine Learning (ML) has witnessed significant expansion, with its applications spanning various domains such as finance, healthcare, and cybersecurity. However, this expansion has brought about significant challenges concerning privacy and security, particularly as ML models deal with sensitive data. Both organizations and individuals have reservations about sharing proprietary or personal information, primarily due to concerns about potential data breaches and misuse. Furthermore, doubts surrounding model integrity and transparency have raised questions about the reliability of ML predictions. To address these issues, Zero Knowledge Proofs (ZKPs) have emerged as a promising cryptographic technique. ZKPs allow secure computations on encrypted data without revealing any sensitive information. They enable a prover to convince a verifier of a statement’s truthfulness without disclosing any underlying data, thereby ensuring data privacy and confidentiality. The paper under consideration conducts an extensive analysis of the burgeoning field of Zero Knowledge Proofs within the context of machine learning applications. It emphasizes the advantages and limitations of ZKPs in preserving data privacy, ensuring computation integrity, and enhancing the security of machine learning systems. The ultimate goal is to foster a deeper understanding of the potentials and challenges associated with integrating ZKPs into modern ML workflows and systems.