Data Protection for the Internet of Things

The Internet of Things (abbreviated: “IoT”) is acknowledged as one of the most important disruptive technologies with more than 16 billion devices forecasted to interact autonomously by 2020. The idea is simple, devices will help to measure the status of physical objects. The devices, containing sensors and actuators, are so small that they can be integrated or attached to any object in order to measure that object and possibly change its status accordingly. A process or work flow is then able to interact with those devices and to control the objects physically. The result is the collection of massive data in a ubiquitous form. This data can be analysed to gain new insights, a benefit propagated by the “Big Data” and “Smart Data” paradigms. While governments, cities and industries are heavily involved in the Internet of Things, society’s privacy awareness and the concerns over data protection in IoT increase steadily. The scale of the collection, processing and dissemination of possibly private information in the Internet of Things has long begun to raise privacy concerns. The problem is a fundamental one, it is the massive data collection that benefits the investment on IoT, while it contradicts the interest on data minimization coming from privacy advocates. And the challenges go even further, while privacy is an actively researched topic with a mature variety of privacy preserving mechanisms, legal studies and surveillance studies in specific contexts, investigations of how to apply this concepts in the constrained environment of IoT have merely begun. Thus the objective of this thesis is threefold and tackles several topics, looking at them in a differentiated way and later bringing them together for one of the first, (more) complete pictures of privacy in IoT. The first starting point is the throughout study of stakeholders, impact areas and proposals on an architectural reference model for IoT. At the time of this writing, IoT was adversed heavily by several companies, products and even governments, creating a blurred picture of what IoT really is. This thesis surveys stakeholders, scenarios, architecture paradigms and definitions to find a working definition for IoT which adequately describes the intersection between all of the aforementioned topics. In a further step, the definition is applied exemplary on two scenarios to identify the common building blocks of those scenarios and of IoT in general. The building blocks are then verified against a similar approach by the IoT-A and Rerum projects and unified to an IoT domain model. This approach purposefully uses notions and paradigms provided in related scientific work and European projects in order to benefit from existing efforts and to achieve a common understanding. In this thesis, the observation of so called cyber-physical properties of IoT leads to the conclusion that IoT proposals miss a core concept of physical interaction in the “real world”. Accordingly, this thesis takes a detour to jurisdiction and identifies ownership and possession as a main concept of “human-to-object” relationships. The analysis of IoT building blocks ends with an enhanced IoT domain model. The next step breaks down “privacy by design”. Notably hereby is that privacy by design has been well integrated in to the new European General Data Protection Regulation (GDPR). This regulation heavily affects IoT and thus serves as the main source of privacy requirements. Gürses et al.’s privacy paradigm (privacy as confidentiality, privacy as control and privacy as practice) is used for the breakdown, preceded by a survey of relevant privacy proposals, where relevancy was measured upon previously identified IoT impact areas and stakeholders. Independently from IoT, this thesis shows that privacy engineering is a task that still needs to be well understood. A privacy development lifecycle was therefore sketched as a first step in this direction. Existing privacy technologies are part of the survey. Current research is summed up to show that while many schemes exist, few are adequate for actual application in IoT due to their high energy or computational consumption and high implementation costs (most notably caused by the implementation of special arithmetics). In an effort to give a first direction on possible new privacy enhancing technologies for IoT, new technical schemes are presented, formally verified and evaluated. The proposals comprise schemes, among others, on relaxed integrity protection, privacy friendly authentication and authorization as well as geo-location privacy. The schemes are presented to industry partners with positive results. This technologies have thus been published in academia and as intellectual property items. This thesis concludes by bringing privacy and IoT together. The final result is a privacy enhanced IoT domain model accompanied by a set of assumptions regarding stakeholders, economic impacts, economic and technical constraints as well as formally verified and evaluated proof of concept technologies for privacy in IoT. There is justifiable interest in IoT as it helps to tackle many future challenges found in several impact areas. At the same time, IoT impacts the stakeholders that participate in those areas, creating the need for unification of IoT and privacy. This thesis shows that technical and economic constraints do not impede such a process, although the process has merely begun

Security of Big Data over IoT Environment by Integration of Deep Learning and Optimization

This is especially true given the spread of IoT, which makes it possible for two-way communication between various electronic devices and is therefore essential to contemporary living. However, it has been shown that IoT may be readily exploited. There is a need to develop new technology or combine existing ones to address these security issues. DL, a kind of ML, has been used in earlier studies to discover security breaches with good results. IoT device data is abundant, diverse, and trustworthy. Thus, improved performance and data management are attainable with help of big data technology. The current state of IoT security, big data, and deep learning led to an all-encompassing study of the topic. This study examines the interrelationships of big data, IoT security, and DL technologies, and draws parallels between these three areas. Technical works in all three fields have been compared, allowing for the development of a thematic taxonomy. Finally, we have laid the groundwork for further investigation into IoT security concerns by identifying and assessing the obstacles inherent in using DL for security utilizing big data. The security of large data has been taken into consideration in this article by categorizing various dangers using a deep learning method. The purpose of optimization is to raise both accuracy and performance