# Special Issue on Security and Privacy in Blockchains and the IoT
###### Abstract
Unlike blockchain-based approaches, IOTA is highly scalable, as restrictions on block size or mining costs are not an issue. As a result, it can also be used to share large amounts of sensor data at a rapid rate. The masked authenticated messaging (MAM) extension for IOTA is used to ensure confidentiality. With MAM, data streams can be sent encrypted as transactions with zero additional cost. Furthermore, MAM provides data subjects with fine-grained access control, allowing them to revoke access to their data at any given time. The authors demonstrate the high potential of IOTA and MAM when dealing with sensitive IoT data by means of an environmental monitoring application. Reviews.Two literature reviews on in-app activity recognition based on encrypted traffic flow segments and on application areas for blockchain technologies in ambient assisted living wrap up this Special Issue. As the adoption of IoT technologies across all areas of life becomes more and more prevalent, not only the extent of data collection but also the network traffic increases. This is due to the fact that IoT applications do not carry out data processing on the IoT devices themselves, but in a powerful backend. As a result, these applications have to send their data to the backend on a continuous basis. Typically, this data stream is encrypted to ensure that third parties do not gain insight into the transferred payload data. However, even encrypted traffic flow segments still allow conclusions to be drawn about in-app activities, which compromises the privacy of the user. In their review, [PERSON] et al. [7] therefore investigate which types of traffic classification exist in the literature. Essentially, there are statistical methods and approaches based on neural networks. In addition to this literature review, the authors also propose their own approach to user activity detection based on in-app data. To this end, they apply an image-based method. Instead of analyzing the network traffic itself, they transform the detected patterns into images, where each pixel stands for features and corresponding feature values of the traffic data. For eight popular mobile applications (e.g., Facebook, Instagram, and WhatsApp), the authors record the network traffic generated by typical in-app activities (e.g., post an image, like an image, and send a short text message). These samples are cleansed, pre-processed, and transformed, resulting in a comprehensive database with characteristic images for each of the in-app activities. A convolutional neural network (CNN) is trained with this image database. The CNN is able to classify activities based on their network traffics with an accuracy of 88 % to 92 %. One sector that benefits significantly from the IoT and the accompanying comprehensive data collection is the healthcare sector. In particular, recurring routine medical checkups, for instance, in the case of chronic diseases, can be carried out remotely, thus relieving both patients and physicians. As a result of the COVID-19 pandemic, there are increased demands to provide assisted care services remotely. Ambient assisted living (AAL) uses IoT technologies to provide non-intrusive support for the daily lives of elderly or disabled people without the need for a caregiver on site. Since the monitoring required for this purpose collects a large amount of highly personal data, there are justified security and privacy concerns regarding data management. The strategic use of blockchain technologies has the potential to alleviate these concerns. [PERSON] et al. [8] therefore conduct a systematic literature review which aims to identify fields of application for blockchain technologies in the AAL and to highlight advantages and open issues in this context. For this purpose, they selected a literature corpus of 472 scientific papers published in high-quality conferences and journals. In a systematic approach following the PRISM process flow, they condensed this overall corpus to the most relevant papers.