The following bachelor thesis explores the design of a GDPR (General Data Protection Regulation) compliant graphical user interface, for an administrative school system. The work presents the process of developing and evaluating a web-based prototype, a platform chosen because of its availability. The aim is to investigate if the design increases the caregivers perception of being in control over personal data, both their own and data related to children in their care. The methods for investigating this subject are grounded in real world research, using both quantitative and qualitative methods.   The results indicate that the users perceive the prototype to be useful, easy to use, easy to learn and that they are satisfied with it. The results also point towards the users feeling of control of both their own and their child’s personal data when using the prototype. The users agree that a higher sense of control also increases their sense of security.

The growth of the Internet has opened up tremendous opportunities for cooperative computations where the results depend on the private data inputs of distributed participating parties. In most cases, such computations are performed by multiple mutually untrusting parties. This has led the research community into studying methods for performing computation across the Internet securely and efficiently. This thesis investigates security methods in the search for an optimum solution to privacy- preserving distributed statistical computation problems. For this purpose, the nonparametric sign test algorithm is chosen as a case for study to demonstrate our research methodology. Two privacy-preserving protocol suites using data perturbation techniques and cryptographic primitives are designed. The first protocol suite, i.e. the P22NSTP, is based on five novel data perturbation building blocks, i.e. the random probability density function generation protocol (RpdfGP), the data obscuring protocol (DOP), the secure two-party comparison protocol (STCP), the data extraction protocol (DEP) and the permutation reverse protocol (PRP). This protocol suite enables two parties to efficiently and securely perform the sign test computation without the use of a third party. The second protocol suite, i.e. the P22NSTC, uses an additively homomorphic encryption scheme and two novel building blocks, i.e. the data separation protocol (DSP) and data randomization protocol (DRP). With some assistance from an on-line STTP, this protocol suite provides an alternative solution for two parties to achieve a secure privacy-preserving nonparametric sign test computation. These two protocol suites have been implemented using MATLAB software. Their implementations are evaluated and compared against the sign test computation algorithm on an ideal trusted third party model (TTP-NST) in terms of security, computation and communication overheads and protocol execution times. By managing the level of noise data item addition, the P22NSTP can achieve specific levels of privacy protection to fit particular computation scenarios. Alternatively, the P22NSTC provides a more secure solution than the P22NSTP by employing an on-line STTP. The level of privacy protection relies on the use of an additively homomorphic encryption scheme, DSP and DRP. A four-phase privacy-preserving transformation methodology has also been demonstrated; it includes data privacy definition, statistical algorithm decomposition, solution design and solution implementation.

Dans cette thèse, nous nous proposons de contribuer à la définition de systèmes de crypto-calculs pour la manipulation en aveugle de données confidentielles. L’objectif particulier de ce travail est l’amélioration des performances du chiffrement homomorphe. La problématique principale réside dans la définition d’une approche d’accélération qui reste adaptable aux différents cas applicatifs de ces chiffrements, et qui, de ce fait, est cohérente avec la grande variété des paramétrages. C’est dans cet objectif que cette thèse présente l’exploration d’une architecture hybride de calcul pour l’accélération du chiffrement de Fan et Vercauteren (FV).Cette proposition résulte d’une analyse de la complexité mémoire et calculatoire du crypto-calcul avec FV. Une partie des contributions rend plus efficace l’adéquation d’un système non-positionnel de représentation des nombres (RNS) avec la multiplication de polynôme par transformée de Fourier sur corps finis (NTT). Les opérations propres au RNS, facilement parallélisables, sont accélérées par une unité de calcul SIMD type GPU. Les opérations de NTT à la base des multiplications de polynôme sont implémentées sur matériel dédié de type FPGA. Des contributions spécifiques viennent en soutien de cette proposition en réduisant le coût mémoire et le coût des communications pour la gestion des facteurs de rotation des NTT.Cette thèse ouvre des perspectives pour la définition de micro-serveurs pour la manipulation de données confidentielles à base de chiffrement homomorphe. / In this thesis, we propose to contribute to the definition of encrypted-computing systems for the secure handling of private data. The particular objective of this work is to improve the performance of homomorphic encryption. The main problem lies in the definition of an acceleration approach that remains adaptable to the different application cases of these encryptions, and which is therefore consistent with the wide variety of parameters. It is for that objective that this thesis presents the exploration of a hybrid computing architecture for accelerating Fan and Vercauteren’s encryption scheme (FV).This proposal is the result of an analysis of the memory and computational complexity of crypto-calculation with FV. Some of the contributions make the adequacy of a non-positional number representation system (RNS) with polynomial multiplication Fourier transform over finite-fields (NTT) more effective. RNS-specific operations, inherently embedding parallelism, are accelerated on a SIMD computing unit such as GPU. NTT-based polynomial multiplications are implemented on dedicated hardware such as FPGA. Specific contributions support this proposal by reducing the storage and the communication costs for handling the NTTs’ twiddle factors.This thesis opens up perspectives for the definition of micro-servers for the manipulation of private data based on homomorphic encryption.

Les bénéfices engendrés par les études statistiques sur les données personnelles des individus sont nombreux, que ce soit dans le médical, l'énergie ou la gestion du trafic urbain pour n'en citer que quelques-uns. Les initiatives publiques de smart-disclosure et d'ouverture des données rendent ces études statistiques indispensables pour les institutions et industries tout autour du globe. Cependant, ces calculs peuvent exposer les données personnelles des individus, portant ainsi atteinte à leur vie privée. Les individus sont alors de plus en plus réticent à participer à des études statistiques malgré les protections garanties par les instituts. Pour retrouver la confiance des individus, il devient nécessaire de proposer dessolutions de user empowerment, c'est-à-dire permettre à chaque utilisateur de contrôler les paramètres de protection des données personnelles les concernant qui sont utilisées pour des calculs.Cette thèse développe donc un nouveau concept d'anonymisation personnalisé, basé sur la généralisation de données et sur le user empowerment.En premier lieu, ce manuscrit propose une nouvelle approche mettant en avant la personnalisation des protections de la vie privée par les individus, lors de calculs d'agrégation dans une base de données. De cette façon les individus peuvent fournir des données de précision variable, en fonction de leur perception du risque. De plus, nous utilisons une architecture décentralisée basée sur du matériel sécurisé assurant ainsi les garanties de respect de la vie privée tout au long des opérations d'agrégation.En deuxième lieu, ce manuscrit étudie la personnalisations des garanties d'anonymat lors de la publication de jeux de données anonymisés. Nous proposons l'adaptation d'heuristiques existantes ainsi qu'une nouvelle approche basée sur la programmation par contraintes. Des expérimentations ont été menées pour étudier l'impact d’une telle personnalisation sur la qualité des données. Les contraintes d’anonymat ont été construites et simulées de façon réaliste en se basant sur des résultats d'études sociologiques. / The benefit of performing Big data computations over individual’s microdata is manifold, in the medical, energy or transportation fields to cite only a few, and this interest is growing with the emergence of smart-disclosure initiatives around the world. However, these computations often expose microdata to privacy leakages, explaining the reluctance of individuals to participate in studies despite the privacy guarantees promised by statistical institutes. To regain indivuals’trust, it becomes essential to propose user empowerment solutions, that is to say allowing individuals to control the privacy parameter used to make computations over their microdata.This work proposes a novel concept of personalized anonymisation based on data generalization and user empowerment.Firstly, this manuscript proposes a novel approach to push personalized privacy guarantees in the processing of database queries so that individuals can disclose different amounts of information (i.e. data at different levels of accuracy) depending on their own perception of the risk. Moreover, we propose a decentralized computing infrastructure based on secure hardware enforcing these personalized privacy guarantees all along the query execution process.Secondly, this manuscript studies the personalization of anonymity guarantees when publishing data. We propose the adaptation of existing heuristics and a new approach based on constraint programming. Experiments have been done to show the impact of such personalization on the data quality. Individuals’privacy constraints have been built and realistically using social statistic studies

This study analyzes the recommendation systems from a user’s perspective and identifies five areas of concern in developing and using a recommendation system. The study’s methods are focus group discussions with Data scientists and Full-stack developers working in the industry. An online survey was distributed to several Facebook groups of various universities. The study results indicate that users have a strong desire to have their moral sensitivities under their control. The study also enables the system developers to understand the recommendations of the system affect the conflicting interests of various entities. / Den här studien analyserar rekommendationssystemen ur ett användarperspektiv, och identifierar fem relevanta områden att ha i åtanke i utvecklingen och användandet av ett rekommendationssystem. Studiens metoder består av fokusgruppsdiskussioner med datavetare och s.k. “full-stack-utvecklare” som arbetar inom IT-branschen. En online-enkät delades ut till flera Facebook-grupper tillhörande olika universitet. Studiens resultat indikerar att användare har en tydlig preferens att ha kontroll över sina moraliska perspektiv. Vidare tillåter även studien systemutvecklare att förstå att systemets rekommendationer påverkar intressekonflikter mellan olika enheter och intressenter.

Background: Data privacy and security has been a big concern in recent years. Data privacy is a concern for everybody who owns a smartphone or accesses a website. This is due to the applications that have been installed on the device or the cookies that have been acquired via websites in the form of advertising cookies. Advertising cookies within programs or sites that track user content provide access to all of the user’s personal sensitive data. The viability of applying conditional consent to boost consumers’ trust in sharing their data is examined in this study. We assess the societal and technological implications of conditional consent implementation. This is accomplished by integrating a third option – maybe – into the access control mechanism.  Research Idea: After reviewing all of the issues concerning user privacy breaches in android applications, we came up with the idea of implementing a Maybe option in which the user can grant access to the permissions for a specified period of time and then automatically disable those permissions at the end of that period. Objectives and Research Methods: The primary goal of our work is to determine the feasibility of implementing partial consent on Android applications, as well as how users understand and are willing to use this suggested option. We chose Experiment, Systematic mapping study, and survey as our study methods. Results: We built a permissions application prototype and provided an option maybe where the user may grant rights for a certain period of time and then automatically deactivate the permissions. Using a poll, many people chose the offered choice and fully comprehended the Maybe option. Conclusions: We understood the usability aspect of the proposed option. The respondents accepted the proposed option and felt the desire for the proposed option. This can cause a change in the security aspects of providing data to the third party applications. Keywords: Partial consent, Access control, Data Privacy, Data Security, Usability Aspect.

Current data privacy-preservation models lack the ability to aid data decision makers in processing datasets for publication. The proposed algorithm allows data processors to simply provide a dataset and state their criteria to recommend an xk-anonymity approach. Additionally, the algorithm can be tailored to a preference and gives the precision range and maximum data loss associated with the recommended approach. This dissertation report outlined the research’s goal, what barriers were overcome, and the limitations of the work’s scope. It highlighted the results from each experiment conducted and how it influenced the creation of the end adaptable algorithm. The xk-anonymity model built upon two foundational privacy models, the k-anonymity and l-diversity models. Overall, this study had many takeaways on data and its power in a dataset.

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Digital technologies’ emergence has changed firms’ innovation and entrepreneurship activities significantly. While the prior literature has investigated how digital technologies stimulate innovation and entrepreneurship, the challenges of the digital transformation process have received limited attention in the information systems (IS) literature. This dissertation aims to examine these challenges by studying policies and governance in the fields of intellectual property, data privacy, and digital infrastructure. In the first essay, I argue that the inefficient protection of employees’ intellectual property rights hampers their innovation activities at work. The second essay evaluates data privacy regulations’ impact on mobile app entrepreneurship. The third essay investigates how mobile platforms’ open policy impedes the adoption of innovative features in operating system (OS) updates. These three essays provide theoretical contributions to the literature on digital transformation, innovation, and entrepreneurship. They also offer practical insights for policymakers and digital infrastructure professionals about how to address digital transformation challenges in innovation and entrepreneurship. / Business Administration/Management Information Systems

Machine learning algorithms based on neural networks have achieved remarkable results and are being extensively used in different domains. However, the machine learning algorithms requires access to raw data which is often privacy sensitive. To address this issue, we develop new techniques to provide solutions for running deep neural networks over encrypted data. In this paper, we develop new techniques to adopt deep neural networks within the practical limitation of current homomorphic encryption schemes. We focus on training and classification of the well-known neural networks and convolutional neural networks. First, we design methods for approximation of the activation functions commonly used in CNNs (i.e. ReLU, Sigmoid, and Tanh) with low degree polynomials which is essential for efficient homomorphic encryption schemes. Then, we train neural networks with the approximation polynomials instead of original activation functions and analyze the performance of the models. Finally, we implement neural networks and convolutional neural networks over encrypted data and measure performance of the models.