Even though public awareness about privacy risks in the Internet is increasing, in the evolution of the Internet to the Internet of Things (IoT) these risks are likely to become more relevant due to the large amount of data collected and processed by the “Things”. The business drivers for exploring ways to monetize such data are one of the challenges identified in this paper for the protection of Privacy in the IoT. Beyond the protection of privacy, this paper highlights the need for new approaches, which grant a more active role to the users of the IoT and which address other potential issues such as the Digital Divide or safety risks. A key facet in ethical design is the transparency of the technology and services in how that technology handles data, as well as providing choice for the user. This paper presents a new approach for users’ interaction with the IoT, which is based on the concept of Ethical Design implemented through a policy-based framework. In the proposed framework, users are provided with wider controls over personal data or the IoT services by selecting specific sets of policies, which can be tailored according to users’ capabilities and to the contexts where they operate. The potential deployment of the framework in a typical IoT context is described with the identification of the main stakeholders and the processes that should be put in place.

In recent years, several research studies have investigated the identification of electronic devices through their physical components and properties, both from a theoretical point of view and through extensive experimental studies. Results have shown that, in many cases, a very high identification accuracy can be obtained by exploiting imperfections and small differences in the electronic components, which are called fingerprints in this context. Part of these studies have focused on a specific category of electronic device, the mobile phone or smartphone, which is usually equipped with components, such as radio frequency frontends, cameras, micro-electro-mechanical systems, microphones, and speakers that are likely to reveal fingerprints in their digital outputs and then allow the identification of the component and of the mobile phone itself. Keeping the focus on mobile phones, this paper provides a survey of the different techniques for mobile phone identification on the basis of their built-in components. This paper describes the methodology, the classification algorithms, and the types of features that are typically used in literature. Outstanding challenges and research issues are also identified and described, together with an overview of the potential applications of mobile phone fingerprinting. In addition, this paper analyzes the potential privacy risks associated to the tracking of the mobile phone on the basis of its fingerprints and the related mitigation techniques. Finally, it summarizes the main issues and identifies research opportunities and potential future trends for mobile phone fingerprinting.

The authors investigate the application of deep convolutional neural networks (CNNs) to the problem of radiometric identification, i.e. the task of authenticating wireless devices on the basis of their radio frequency (RF) emissions, which contain features directly related to the physical properties of the wireless devices. They collected digitised RF from 12 wireless devices, and used various techniques to transform the time series derived from the RF to images. A deep CNN is then applied to the images. The authors' results show that the identification performance of the combination of deep CNN with an image representation significantly outperforms conventional methods based on dissimilarity on the original time series. Moreover, a specific comparison among RF‐to‐image techniques show that on their datasets the wavelet‐based approach outperforms other approaches, also in the presence of white Gaussian noise.

Our vision in this paper is that agency, as the individual ability to intervene and tailor the system, is a crucial element in building trust in IoT technologies. Following up on this vision, we will first address the issue of agency, namely the individual capability to adopt free decisions, as a relevant driver in building trusted human-IoT relations, and how agency should be embedded in digital systems. Then we present the main challenges posed by existing approaches to implement this vision. We show then our proposal for a model-based approach that realizes the agency concept, including a prototype implementation.

Illegal manipulation (i.e., tampering) of vehicles is a severe problem because vehicle emissions increase orders of magnitude and significantly impact the environment and human health. This study measured the emissions before and after representative approaches of tampering of two Euro 6 Diesel light-duty passenger cars, two Euro VI Diesel heavy-duty trucks, and a Stage IV Diesel non-road mobile machinery (NRMM) agricultural tractor. With tampering of the selective catalytic reduction (SCR) for NO_x, the NO_x emissions increased by more than one order of magnitude exceeding 1000 mg/km (or mg/kWh) for all vehicles, reaching older Euro or even pre-Euro levels. The tampering of the NO_x sensor resulted in relatively low NO_x increases, but significant ammonia (NH₃) slip. The particle number emissions increased three to four orders of magnitude, reaching 6–10 × 10¹² #/km for the passenger car (one order of magnitude higher than the current regulation limit). The tampered passenger car’s NO_x and particle number emissions were one order of magnitude higher even compared to the emissions during a regeneration event. This study confirmed that (i) tampering with the help of an expert technician is still possible, even for vehicles complying with the current Euro standards, although this is not allowed by the regulation; (ii) tampering results in extreme increases in emissions.

The correct identification of smartphones has various applications in the field of security or the fight against counterfeiting. As the level of sophistication in counterfeit electronics increases, detection procedures must become more accurate but also not destructive for the smartphone under testing. Some components of the smartphone are more likely to reveal their authenticity even without a physical inspection, since they are characterized by hardware fingerprints detectable by simply examining the data they provide. This is the case of MEMS (Micro Electro-Mechanical Systems) components like accelerometers and gyroscopes, where tiny differences and imprecisions in the manufacturing process determine unique patterns in the data output. In this paper, we present the experimental evaluation of the identification of smartphones through their built-in MEMS components. In our study, three different phones of the same model are subject to repeatable movements (composing a repeatable scenario) using an high precision robotic arm. The measurements from MEMS for each repeatable scenario are collected and analyzed. The identification algorithm is based on the extraction of the statistical features of the collected data for each scenario. The features are used in a support vector machine (SVM) classifier to identify the smartphone. The results of the evaluation are presented for different combinations of features and Inertial Measurement Unit (IMU) outputs, which show that detection accuracy of higher than 90% is achievable.