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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. 

Smart IoT-based systems often desire continuous execution of multiple latency-sensitive Deep Learning (DL) appli- cations. The edge servers serve as the cornerstone of such IoT- based systems, however, their resource limitations hamper the continuous execution of multiple (multi-tenant) DL applications. The challenge is that, DL applications function based on bulky “neural network (NN) models” that cannot be simultaneously maintained in the limited memory space of the edge. Accordingly, the main contribution of this research is to overcome the memory contention challenge, thereby, meeting the latency constraints of the DL applications without compromising their inference accuracy. We propose an efficient NN model management frame- work, called Edge-MultiAI, that ushers the NN models of the DL applications into the edge memory such that the degree of multi-tenancy and the number of warm-starts are maximized. Edge-MultiAI leverages NN model compression techniques, such as model quantization, and dynamically loads NN models for DL applications to stimulate multi-tenancy on the edge server. We also devise a model management heuristic for Edge-MultiAI, called iWS-BFE, that functions based on the Bayesian theory to predict the inference requests for multi-tenant applications, and uses it to choose the appropriate NN models for loading, hence, increasing the number of warm-start inferences. We evaluate the efficacy and robustness of Edge-MultiAI under various configurations. The results reveal that Edge-MultiAI can stimulate the degree of multi-tenancy on the edge by at least 2× and increase the number of warm-starts by ≈ 60% without any major loss on the inference accuracy of the applications.