More and more devices and sensors are around us in today's world. There is an increasing tendency to connect devices and sensors to the Internet. We call such network the Internet of Things. As the cost of computing power continues to decrease, data collection and analysis becomes cheaper. Thus, we are able to get much better insight into different domains. This could be a problem in commercial sector, where we do not want to compromise proprietary know how or data. Especially big concerns are around personal data. We can now analyse behavior of people or get access to personal information such as health or spending patterns. This thesis addresses these privacy issues. Powered by TCPDF (www.tcpdf.org)

The superior reconfigurability of software defined radio mobile devices has made it one of the most promising technology on the wireless network and in the mobile communication industry. The evolution from a static and rigid system to a highly dynamic environment, which offers many advantages over current systems, has been made possible thanks to the concepts of programmability and reconfigurability introduced by the software defined radio technology and the higher level of flexibility and openness of this technology's devices. Clearly, the software defined radio mobile device's flexibility is a great advantage since the customer is able to use the same device in different parts of the world, with different wireless technologies. Despite the advantages, there are still issues to be discussed regarding security. According to the Software Defined Radio Forum some of the concerns are the radio configuration download, storage and installation, user's privacy, and cloning. To address the SDR Forum concerns a raud-prevention framework is proposed. The framework is composed by new pieces of hardware, new modules and new protocols that together greatly enhance the overall security of software defined radio mobile devices and this new highly dynamic environment. The framework offers security monitoring against malicious attacks and viruses that may affect the configuration data; protects sensitive information through the use of protected storage; creates and protects an identity for the system; employs a secure and efficient protocol for radio configuration download and update; and finally, establishes an anti-cloning scheme, which not only guarantees that no units can be cloned over the air but also elevates the level of difficulty to clone units if the attacker has physical access to those units. Even if cloned units exist, the anti-cloning scheme is able to identify them and deny any service.

When internet-mediated messaging and social networking applications were introduced in mobile phones, mobile messaging entered a new paradigm. Users can easily create profiles and communicate with other users instantly at anytime of day and anywhere in the world. This paradigm however, has brought forward new privacy issues. Over the course of this exegesis I aimed to explore different aspects of privacy issues related to mobile messaging, including users' awareness and the control of the privacy issues when using social networking messaging. This was demonstrated by conducting a project, which involved development of a mobile phone application and a role-playing game. The project created a frictional scenario of users communicating via mobile messaging. My research revealed that the participants lacked awareness of privacy violations when using mobile messaging, and tended not to modify their behaviours immediately when privacy breaches arose. This research contributes to the existing body of research in the area of mobile messaging privacy, and advocated user awareness of privacy issues in this domain. It could potentially be used as a base for future research.

Background The development of the Internet has changed the direction of advertising. Personalised advertising has emerged as the most effective and most profitable form of advertisement. Although personalised advertisement has been praised by many, privacy related concerns have been raised since it can be perceived as a violation of the consumer's privacy. Online shopping is another activity growing on the Internet, which also raises concerns about privacy. Therefore, we find it interesting to look on how personalised advertisement in relation to privacy issues can affect the consumer when buying online. Purpose The purpose of this thesis is to investigate how online shoppers respond to privacy issues and personalised advertising. The research will focus on the shoppers’ perspective of privacy issues with personalised advertising online. It will also provide companies with useful insights into customer behaviour when shopping online, with the intention of evaluating various forms of personalised advertisement and their effectiveness in order to strengthen companies’ online personalised marketing campaigns. Method As multiple explanations to one and the same research question are sought, the research will be conducted from an interpretivist standpoint. A qualitative research method has been chosen in the form of twelve semi-structured interviews with individuals who have been residents of Sweden for more than six months, with a wide range of ages and an equal representation of the male and female gender. The empirical findings will be analysed based on the grounded theory where common themes based on similarities will be presented. Conclusion It was concluded that the attitudes towards personalised advertisement, privacy issues and its effect on buying behaviour depends on where the advertisement comes from as well as from where the individual is shopping. Furthermore, it was also found that although privacy concerns were raised it did not prevent individuals from continuing to purchase online, only from what sites the purchasing was conducted.

Background: Companies collect private data about consumers for marketingpurposes. Mobile devices provide marketers with valuable insights intoconsumers' hyper-context information concerning specific consumersituations such as location, time, and environment. Consumers, on the otherhand, may have a different attitude towards how marketers use their privatedata. Purpose: The purpose of this thesis is to analyse and gain insight into theemotional experience of consumers when receiving personalized marketingmessages that are designed using their private data collected from mobiledevices. Method: A qualitative study was implemented and primary data werecollected from semi-structured interviews. The conducted data were analysedto understand the underlying concepts of this thesis research question. Anabductive approach was used and involved back-and-forth engagement withthe empirical findings of the social world for theoretical ideas and with thesecondary sources of literature. Conclusion: Consumers’ can experience both positive and negative feelingssimultaneously. The authors conclude that it was not the personalizedmarketing messages themselves that created negative emotions amongconsumers, it was the knowledge and the feeling of knowing that theadvertisements were created from their 'private data. The positive emotionswere connected to the benefits of receiving advertisements that matched theirinterests.

We present a theory for constructing linear, black box approximations to face recognition algorithms and empirically demonstrate that a surprisingly diverse set of face recognition approaches can be approximated well using a linear model. The construction of the linear model to a face recognition algorithm involves embedding of a training set of face images constrained by the distances between them, as computed by the face recognition algorithm being approximated. We accomplish this embedding by iterative majorization, initialized by classical multi-dimensional scaling (MDS). We empirically demonstrate the adequacy of the linear model using six face recognition algorithms, spanning both template based and feature based approaches on standard face recognition benchmarks such as the Facial Recognition Technology (FERET) and Face Recognition Grand Challenge (FRGC) data sets. The experimental results show that the average Error in Modeling for six algorithms is 6.3% at 0.001 False Acceptance Rate (FAR), for FERET fafb probe set which contains maximum number of subjects among all the probe sets. We demonstrate the usefulness of the linear model for algorithm dependent indexing of face databases and find that it results in more than 20 times reduction in face comparisons for Bayesian Intra/Extra-class person classifier (BAY), Elastic Bunch Graph Matching algorithm (EBGM), and the commercial face recognition algorithms. We also propose a novel paradigm to reconstruct face templates from match scores using the linear model and use the reconstructed templates to explore the security breach in a face recognition system. We evaluate the proposed template reconstruction scheme using three, fundamentally different, face recognition algorithms: Principal Component Analysis (PCA), Bayesian Intra/Extra-class person classifier (BAY), and a feature based commercial algorithm. With an operational point set at 1% False Acceptance Rate (FAR) and 99% True Acceptance Rate (TAR) for 1196 enrollments (FERET gallery), we show that at most 600 attempts (score computations) are required to achieve 73%, 72% and 100% chance of breaking in as a randomly chosen target subject for the commercial, BAY and PCA based face recognition system, respectively. We also show that the proposed reconstruction scheme has 47% more probability of breaking in as a randomly chosen target subject for the commercial system as compared to a hill climbing approach with the same number of attempts.