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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.

Dans ce document, nous présentons une nouvelle approche en apprentissage machine pour la classification. Le cadre que nous proposons est basé sur des circuits booléens, plus précisément le classifieur produit par notre algorithme a cette forme. L’utilisation des bits et des portes logiques permet à l’algorithme d’apprentissage et au classifieur d’utiliser des opérations vectorielles binaires très efficaces. La qualité du classifieur, produit par notre approche, se compare très favorablement à ceux qui sont produits par des techniques classiques, à la fois en termes d’efficacité et de précision. En outre, notre approche peut être utilisée dans un contexte où la confidentialité est une nécessité, par exemple, nous pouvons classer des données privées. Ceci est possible car le calcul ne peut être effectué que par des circuits booléens et les données chiffrées sont quantifiées en bits. De plus, en supposant que le classifieur a été déjà entraîné, il peut être alors facilement implémenté sur un FPGA car ces circuits sont également basés sur des portes logiques et des opérations binaires. Par conséquent, notre modèle peut être facilement intégré dans des systèmes de classification en temps réel. / In this document we present a novel approach in machine learning for classification. The framework we propose is based on boolean circuits, more specifically the classifier produced by our algorithm has that form. Using bits and boolean gates enable the learning algorithm and the classifier to use very efficient boolean vector operations. The accuracy of the classifier we obtain with our framework compares very favourably with those produced by conventional techniques, both in terms of efficiency and accuracy. Furthermore, the framework can be used in a context where information privacy is a necessity, for example we can classify private data. This can be done because computation can be performed only through boolean circuits as encrypted data is quantized in bits. Moreover, assuming that the classifier was trained, it can then be easily implemented on FPGAs (i.e., Field-programmable gate array) as those circuits are also based on logic gates and bitwise operations. Therefore, our model can be easily integrated in real-time classification systems.

This work proposes the system for dynamic enforcement of access rights on Android. Each suspicious application can be repackaged by this system, so that the access to selected private data is restricted for the outer world. The system intercepts the system calls using Aurasium framework and adds an innovative approach of tracking the information flows from the privacy-sensitive sources using tainting mechanism without need of administrator rights. There has been designed file-level and data-level taint propagation and policy enforcement based on Android binder.

The goal of this thesis is to explore existing solutions of closed and open data sharing, propose options of sharing non-public data, implement selected solution and demonstrate the functionality of the system for sharing closed data. Implementation output consist of a catalog of non-public datasets, web application for administration of non-public datasets, application interface gateway and demonstration application.