While the newly envisioned Smart(er) Grid (SG) will result in a more efficient and reliable power grid, its collection and use of fine-grained meter data has widely raised concerns on consumer privacy. While a number of approaches are available for preserving consumer privacy, these approaches are mostly not very practical to be used due to two reasons: First, since the data is hidden, this reduces the ability of the utility company to use the data for distribution state estimation. Secondly and more importantly, the approaches were not tested under realistic wireless infrastructures that are currently in use. In this thesis, a meter data obfuscation approach to preserve consumer privacy is proposed to implement that has the ability to perform distribution state estimation. Then, its performance on LTE and a large-scale Advanced Metering Infrastructure (AMI) network built upon the new IEEE 802.11s wireless mesh standard are assessed. LTE/EPC(Evolved Packet Core) model is used between the gateway and the utility. EPC's goal is to improve network performance by the separation of control and data planes and through a flattened IP architecture, which reduces the hierarchy between mobile data elements. Using obfuscation values provided via this approach, the meter readings are obfuscated to protect consumer privacy from eavesdroppers and the utility companies while preserving the utility companies' ability to use the data for state estimation.The impact of this approach on data throughput, delay and packet delivery ratio under a variety of conditions are assessed.

This project explores the internet of things (IoT) at home, especially the aspects related to the quantity and the quality of the of data collected by the smart devices and the violation of the users’ privacy this situation represents, since with the help of machine learning algorithms, these devices are capable of storing and analysing information related to the daily routine of each user at home. Therefore, this research enquires new ways to raise the user's’ awareness about the flow of the data within the IoT at home in order to empower them and give them back the status of administrators of this context by designing devices that are capable of obfuscating the data before it leaves the home.During this process, several methods were used together in order to reach the outcomes. From the use of annotated portfolios to evaluate the state of the art related with the field, to video sketching as a useful and quick tool to embrace the user’s perspective in parallel with the use of cultural probes in order to test some conceptual scenarios and find new ways to explore this project based on the experiences of the participants.As a result, this project’s outcome is based on the use of the materialization of the data as the proper way to bring the abstract process that happens in the background closer to the user's reality in order to display how this data is actually flowing through the environment and at the end generate a call­to­action to guide the user into the execution of the obfuscation of the data.This project opens up the discussion within the interaction design field about the way we are communicating with the technology and if it is the proper way to do it when this technology coexist with the user at home. Additionally, it questions the way in which the interfaces should be designed in order to create a transparent dialogue between the users, the objects and the vendors.

Privacy is defined as the freedom from unauthorized intrusion. The availability of personal information through online databases, such as government records, medical records, and voters and #146; lists, pose a threat to personal privacy. The concern over individual privacy has led to the development of legal codes for safeguarding privacy in several countries. However, the ignorance of individuals as well as loopholes in the systems, have led to information breaches even in the presence of such rules and regulations. Protection against data privacy requires modification of the data itself. The term {em data obfuscation} is used to refer to the class of algorithms that modify the values of the data items without distorting the usefulness of the data. The main goal of this thesis is the development of a data obfuscation technique that provides robust privacy protection with minimal loss in usability of the data. Although medical and financial services are two of the major areas where information privacy is a concern, privacy breaches are not restricted to these domains. One of the areas where the concern over data privacy is of growing interest is collaborative filtering. Collaborative filtering systems are being widely used in E-commerce applications to provide recommendations to users regarding products that might be of interest to them. The prediction accuracy of these systems is dependent on the size and accuracy of the data provided by users. However, the lack of sufficient guidelines governing the use and distribution of user data raises concerns over individual privacy. Users often provide the minimal information that is required for accessing these E-commerce services. The lack of rules governing the use and distribution of data disallows sharing of data among different communities for collaborative filtering. The goals of this thesis are (a) the definition of a standard for classifying DO techniques, (b) the development of a robust cluster preserving data obfuscation algorithm, and (c) the design and implementation of a privacy-preserving shared collaborative filtering framework using the data obfuscation algorithm.

The privacy and surveillance issues that are consequences of the Internet of Things are the motivation and grounding for this thesis project. The Internet of Things (IoT) is a scenarioin which physical objects are able to communicate to each other and the environment, by transferring data over communication networks. The IoT allows technology to become smaller and more ubiquitous, and by being integrated in the environment around us, the world is becoming increasingly connected. Even though these developments will generally make our lives easier and more enjoyable, the Internet of Things also faces some challenges. One of these are the aforementioned privacy and surveillance issues that are the results of transferring sensitive data over communication networks. The aim of this thesis project is therefore to answer, both in a theoretical, as well as in a practical way, the following research question: How can the Internet of Things be more accessible and safe for the everyday user? Accordingly, the Auster online platform, the Auster app and the Data Obfuscation Kit were developed to provide people with the tools and knowledge to construct home automation projects themselves, as an alternative for using applications from governments and corporations alike. The aim is to create a way to endow people with the capability to exploit their talents, realise their visions and share this with a community joining forces. By enabling people to create their own home automation projects, personal data is kept in the user’s possession and the collection of data by governments and companies alike is prevented. Moreover, by giving the control over technology back to the user, creativity and innovation in the field of the Internet of Things in domestic environments are expected to increase.