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ABSTRACT BackgroundConfidential 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. ObjectiveProvided 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. MethodsWe 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. ResultsWhile 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. ConclusionProviding 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. 

Along with the rise of domain‐specific computing (ASICs hardware) and domain‐specific programming languages, we envision that the next step is the emergence of domain‐specific cloud platforms. Considering multimedia streaming as one of the most trendy applications in the IT industry, the goal of this study is to develop serverless multimedia streaming engine (SMSE), the first domain‐specific serverless platform for multimedia streaming. SMSE democratizes multimedia service development via enabling content providers (or even end‐users) to rapidly develop their desired functionalities on their multimedia contents. Upon developing SMSE, the next goal of this study is to deal with its efficiency challenges and develop a function container provisioning method that can efficiently utilize cloud resources and improve the users' quality of service. In particular, we develop a dynamic method that provisions durable or ephemeral containers depending on the spatiotemporal and data‐dependency characteristics of the functions. Evaluating the prototype implementation of SMSE under real‐world settings demonstrates its capability to reduce both the containerization overhead, and the makespan time of serving multimedia processing functions (by up to 30%) in compare to the function provision methods that are being used in the general‐purpose serverless cloud systems.