@Article{cmc.2026.087848,
AUTHOR = {Quang-Vinh Dang, Dat Le, Minh Ngoc Dinh, Ngoc-Son-An Nguyen},
TITLE = {Privacy-Preserving Cryptography for Machine Learning: A Comprehensive Review of Homomorphic Encryption, Differential Privacy, Secure Computation, and Post-Quantum Foundations},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/28246},
ISSN = {1546-2226},
ABSTRACT = {Deploying machine learning on sensitive data has made privacy a first-order design constraint. Over the past five years privacy-preserving machine learning (PPML) has matured from isolated proofs of concept into an ecosystem of cryptographic and statistical techniques, each occupying a distinct point in the trade-off space among confidentiality, integrity, utility, cost and trust. This review surveys that ecosystem for a research audience. We examine homomorphic encryption, including several currently available bootstrapping styles and encrypted transformer inference; differential privacy in deep learning, analytics and the private fine-tuning of large language models; privacy budget management through composition theorems, numerical accountants and empirical auditing; secure multi-party computation; zero-knowledge proofs as an integrity complement to confidentiality; federated learning, covering secure aggregation, gradient-leakage attacks, verifiable aggregation and federated adaptation of large models; encrypted search and its leakage-abuse attacks; and post-quantum foundations following the 2024 standardization of Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM), Module-Lattice-Based Digital Signature Algorithm (ML-DSA) and Stateless Hash-Based Digital Signature Algorithm (SLH-DSA). Beyond surveying these areas, we contribute three syntheses intended to make the material actionable: an analysis of compositional compatibility identifying where combining techniques creates conflicts rather than complementarity; a scenario-driven selection guide mapping deployment situations to technique combinations; and an examination of the gap between research prototypes and production systems spanning engineering complexity, key management, regulatory compliance and evidence from healthcare, finance and smart-city deployments. We close by identifying the open problems that will shape the next phase of research.},
DOI = {10.32604/cmc.2026.087848}
}