Confidential Computing Across Edge‐To‐Cloud for Machine Learning: A Survey Study

ABSTRACT Background Confidential computing has gained prominence due to the escalating volume of data‐driven applications (e.g., machine learning and big data) and the acute desire for secure processing of sensitive data, particularly across distributed environments, such as the edge‐to‐cloud continuum. Objective Provided that the works accomplished in this emerging area are scattered across various research fields, this paper aims at surveying the fundamental concepts and cutting‐edge software and hardware solutions developed for confidential computing using trusted execution environments, homomorphic encryption, and secure enclaves. Methods We underscore the significance of building trust at both the hardware and software levels and delve into their applications, particularly for regular and advanced machine learning (ML) (e.g., large language models (LLMs), computer vision) applications. Results While substantial progress has been made, there are some barely‐explored areas that need extra attention from the researchers and practitioners in the community to improve confidentiality aspects, develop more robust attestation mechanisms, and address vulnerabilities of the existing trusted execution environments. Conclusion Providing a comprehensive taxonomy of the confidential computing landscape, this survey enables researchers to advance this field to ultimately ensure the secure processing of users' sensitive data across a multitude of applications and computing tiers.