Trusted Execution Environments have been applied to improve data security in many distinct application scenarios since they enable data processing in a separate and protected region of memory. To investigate how this technology has been applied to the different IoT scenarios, which commonly deal with specific characteristics such as device resource constraints, we carried out a systematic literature review. For this, we selected and analyzed 58 papers from different conferences and journals, identifying the main IoT solutions and scenarios in which TEE has been employed.We also gathered the mentioned TEE advantages and disadvantages as well as the suggestions for future works. This study gives a general overview of the use of TEEs for cloud/fog-based IoT applications, bringing some challenges and directions.

The Internet-of-Things (IoT) scenarios commonly present security and privacy concerns, either due to the processing constraints of devices or the employment of external servers to process and store data, for instance, in cloud-based IoT applications. In this sense, to protect data and decrease user distrust in external entities, security technologies are of utmost importance. Trusted execution environments (TEEs), which process data in an isolated and protected region of memory, are among these technologies. We focus on a trusted architecture solution based on the use of TEEs and the application of authentication, authorization, and encryption mechanisms to protect data in IoT applications. We specified the trusted IoT architecture (TIoTA) using hierarchical colored Petri nets, and performed simulations and model checking of key security properties related to desired and prohibited behaviors, enabling model-based testing. This article enhances the state of the art by providing project artifacts (e.g., executable and parametric models) for correctly implementing the TIoTA and by presenting evidence that the usage of the architecture can improve security and privacy.

The main characteristics of blockchains, such as security and traceability, have enabled their use in many distinct scenarios, such as the rise of new cryptocurrencies and decentralized applications (dApps). However, part of the information exchanged in the typical blockchain is public, which can lead to privacy issues. To avoid or mitigate these issues, some blockchains are applying mechanisms to deal with data privacy. Trusted execution environments, the basis of confidential computing, and secure multi-party computation are two technologies that can be applied in that sense. In this paper, we analyze seven blockchain technologies that apply mechanisms to improve data privacy. We define seven technical questions related to common requirements for decentralized applications and, to answer each question, we review the available documentation and gather information from chat channels. We briefly present each blockchain technology and the answers to each technical question. Finally, we present a table summarizing the information and showing which technologies are more prominent.

With the increasing adoption of the edge computing paradigm, including multi-access edge computing (MEC) in telecommunication scenarios, many works have explored the benefits of adopting it. Since MEC, in general, presents a reduction in latency and energy consumption compared to cloud computing, it has been applied to deploy artificial intelligence services. This kind of service can have distinct requirements, which involve different computational resource capabilities as well different data formats or communication protocols to collect data. In this sense, we propose the VEF Edge Framework, which aims at helping the development and deployment of artificial intelligence services for MEC scenarios considering requirements as low-latency and CPU/memory consumption. We explain the VEF architecture and present experimental results obtained with a base case’s implementation: an object detection inference service deployed with VEF. The experiments measured CPU and memory usage for the VEF’s main components and the processing time for two procedures (inference and video stream handling).