Background: Digital loyalty programs collect extensive personal data, but literature has so far neglected the aspect of privacy concerns within the programs. The privacy paradox denotes the contradictory behavior amongst consumers stating privacy risk beliefs and actual behavior. Existing literature is calling for a dual perspective of the privacy paradox and digital loyalty programs to find the underlying reasons for the contradictory behavior. Purpose: The purpose of this study was to explore (1) if and when privacy concerns existed in digital loyalty programs and (2) why consumers overruled their privacy concerns in digital loyalty programs. Method: A qualitative method with 18 semi-structured interviews were conducted through a non-probability purposive sampling of consumers within digital loyalty programs. The findings were then analyzed through a thematic analysis to finally construct a model based upon the given research purpose.  Conclusion: The findings suggest that consumers experience privacy concerns in digital loyalty programs from external exposure to privacy breaches and when consumers felt their mental construct of terms and conditions were violated. Four themes were found to influence why consumers overrule their privacy concerns and share personal data with digital loyalty programs, relating to cognitive biases, value of rewards received, and digital trust for the program provider. The findings were synthesized into a model illustrating the consumer assessment of personal data sharing in digital loyalty programs and the interconnection between the influences.

Smart environments provide users with a large number of new services that will improve their lives, however, they also have the potential for collecting staggering amounts of personal information, which, if misused, poses a multitude of privacy threats to users ranging from identification, tracking, stalking, monitoring and profiling. Consequently, the users’ right to informational self-determination is at stake in smart environments. Usable Privacy-Enhancing Identity Management (PE-IdM) can re-establish user control by offering users a selection of meaningful privacy preference settings that they could choose from. However, different privacy trade-offs need to be considered and managed for the configuration of the identity management system as well as cultural privacy aspects influencing user's privacy preferences. Guidelines for usable management of privacy settings that address varying end user preferences for control and privacy conflicting goals are needed.   The objective of this thesis is to explore approaches for enforcing usable PE-IdM for smart environments, with a focus on vehicular ad hoc networks (VANETs). To that end, we unravel the technical state of the art regarding the problem space and solutions, as well as investigating users’ privacy preferences cross-culturally in Sweden and South Africa. We elicit requirements for achieving usable PE-IdM, which are based on usable configuration options, offering suitable selectable privacy settings that will cater for the needs and preferences of users with different cultural backgrounds.

<p dir="ltr">Differential privacy (DP) uses a probabilistic framework to measure the level of privacy protection of a mechanism that releases data analysis results to the public. Although DP is widely used by both government and industry, there is still a lack of research on statistical inference under DP guarantees. On the one hand, existing DP mechanisms mainly aim to extract dataset-level information instead of population-level information. On the other hand, DP mechanisms introduce calibrated noises into the released statistics, which often results in sampling distributions more complex and intractable than the non-private ones. This dissertation aims to provide general-purpose methods for statistical inference, such as confidence intervals (CIs) and hypothesis tests (HTs), that satisfy the DP guarantees. </p><p dir="ltr">In the first part of the dissertation, we examine a DP bootstrap procedure that releases multiple private bootstrap estimates to construct DP CIs. We present new DP guarantees for this procedure and propose to use deconvolution with DP bootstrap estimates to derive CIs for inference tasks such as population mean, logistic regression, and quantile regression. Our method achieves the nominal coverage level in both simulations and real-world experiments and offers the first approach to private inference for quantile regression.</p><p dir="ltr">In the second part of the dissertation, we propose to use the simulation-based ``repro sample'' approach to produce CIs and HTs based on DP statistics. Our methodology has finite-sample guarantees and can be applied to a wide variety of private inference problems. It appropriately accounts for biases introduced by DP mechanisms (such as by clamping) and improves over other state-of-the-art inference methods in terms of the coverage and type I error of the private inference. </p><p dir="ltr">In the third part of the dissertation, we design a debiased parametric bootstrap framework for DP statistical inference. We propose the adaptive indirect estimator, a novel simulation-based estimator that is consistent and corrects the clamping bias in the DP mechanisms. We also prove that our estimator has the optimal asymptotic variance among all well-behaved consistent estimators, and the parametric bootstrap results based on our estimator are consistent. Simulation studies show that our framework produces valid DP CIs and HTs in finite sample settings, and it is more efficient than other state-of-the-art methods.</p>

<p> The growing trend of encrypted network traffic is changing the cybersecurity threat scene. Most critical infrastructures and organizations enhance service delivery by embracing digital platforms and applications that use encryption to ensure that data and Information are moved across networks in an encrypted form to improve security. While this protects data confidentiality, hackers are also taking advantage of encrypted network traffic to hide malicious software known as malware that will easily bypass the conventional detection mechanisms on the system because the traffic is not transparent for the monitoring mechanism on the system to analyze. Cybercriminals leverage encryption using cryptographic protocols such as SSL/TLS to launch malicious attacks. This hidden threat exists because of the SSL encryption of benign traffic. Hence, there is a need for visibility in encrypted traffic. This research was conducted to detect malware in encrypted network traffic without decryption. The existing solution involves bulk decryption, analysis, and re-encryption. However, this method is prone to privacy issues, is not cost-efficient, and is time-consuming, creating huge overhead on the network. In addition, limited research exists on detecting malware in encrypted traffic without decryption. There is a need to strike a balance between security and privacy by building an intelligent framework that can detect malicious activity in encrypted network traffic without decrypting the traffic prior to inspection. With the payload still encrypted, the study focuses on extracting metadata from flow features to train the machine-learning model. It further deployed this set of features as input to an autoencoder, leveraging the construction error of the autoencoder for anomaly detection. </p>

I en tid där digitaliseringen är mer utbredd än någonsin ökar också mängden data som samlas och delas online. I takt med att nya tekniker utvecklas öppnas det upp för nya utmaningar för integritetsfrågor. En aktiv användare online ägnar sig med störst sannolikhet också åt ett eller flera sociala medier, där ändamålen ofta innebär att dela med sig av information till andra. Eftersom tekniker Augmented Reality används mer frekvent i några av de största sociala medierapplikationerna blev studiens syfte att undersöka potentiella integritetsproblem med Augmented Reality.l Studiens tillvägagångsätt har bestått av en empirisk datainsamling för att skapa ett teoretiskt ramverk för studien. Utifrån detta har det genomförts en digital enkät samt intervjuer för att närmare undersöka användarens beteende online och The Privacy Paradox. Utifrån undersökningens resultat kunde The Privacy Paradox bekräftas och ge en bättre förståelse för hur användaren agerar genom digitala kanaler. I studien behandlas olika aspekter kring integritetsfrågor såsom användarvillkor, sekretessavtal, datamäklare, framtida konsekvenser och vad tekniken möjliggör. Studien kom fram till att användare, företaget och dagens teknik tillåter att en känsligare information kan utvinnas genom ett dataintrång. Även om det ännu inte har inträffat ett dataintrång som grundat sig i Augmented Reality före denna studie, finns det en risk att det endast handlar om en tidsfråga innan det sker. / In a time when digitalization is more widespread than ever, the amount of data collected and shared is increasing. As new technologies develop, challenges for privacy concerns arises. An active online user is likely to engage in one or many social media platforms, where the purpose often involves sharing information with others. Since Augmented Reality is more frequently supported in some the biggest social media applications, the purpose of this study was to investigate potential privacy concerns with Augmented Reality. The study's approach consisted of an empirical data collection to create a theoretical framework for the study. Based on this, a digital survey and interviews were conducted to further investigate the user's behavior online and The Privacy Paradox. Based on the results of the survey, The Privacy Paradox could be confirmed and a better understanding of how the user interacts through digital channels was achieved. The study treats different aspects of privacy concerns such as user terms, privacy policies, data brokers, future consequences and what technology enables. The study reached the conclusion that users, buisnesses and today's technology allow a more sensitive type of information to be collected through a data breach. Even if there has not yet occurred a data breach enabled by Augmented Reality prior to this study, there is a risk that it is only a matter of time until it happens.

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This thesis examined how aware upper secondary school students are of how the informationthey share on social media platforms is handled, what concerns are raisedregarding the personal data collected and used by corporations and authorities andhow upper secondary school students view the future education of digital privacy.Questions in the thesis are answered by performing a study using questionnaires asa data collection method. The conclusion is that upper secondary school students inSweden are fairly unaware of how their information is handled in the digital worldand what regulations are in place to protect their data. Some concerns are raisedaround the participants’ lack of knowledge and how their trust in authorities haslowered due to digital surveillance, the lack of mitigating actions against abuse ofsurveillance, and the debate regarding mass surveillance. In the future, the participantswant more education about digital privacy, and they believe it should be partof the curriculum in upper secondary school.

Improvement in information communication technology (ICT) is one of the factors behind growth in economic productivity. A major dimension of this is the use of the Internet in e-commerce, allowing companies to collect, store, and exchange personal information obtained from visitors to their websites. Electronic commerce has many different variants, and is believed by many governments throughout the world to be the engine of economic stability in the future. While electronic commerce has many benefits, there is evidence to suggest privacy concerns are an inhibitor to its adoption in Jordan and the UK. According to Campbell (1997, p.45), privacy in this context can be defined as “the ability of individuals to determine the nature and extent of information about them which is being communicated to others”. The importance of information in e-commerce has increased, because the main success factor for the completion of transactions between businesses and consumers is the companies’ ability to access consumers’ personal details. This conflicts with the consumers’ fear of providing personal information to un-trusted parties, which makes them disinterested in entering contracts via the internet. This research discusses privacy concerns as an inhibitor for electronic commerce by providing a comparison between UK and Jordanian regulations, to establish the impact that these regulations have ameliorating privacy concerns regarding the development of electronic commerce in Jordan and the UK. The interpretive grounded theory approach has allowed the researcher to gain a deep understanding about privacy perceptions of electronic commerce held by the main stakeholders: government, businesses and consumers. Furthermore, through implementing the Straussian grounded theory approach as a data collection and analysis method, two grounded theories have emerged as giving deeper understanding of the situation in Jordan and the UK regarding privacy concerns and how this affects electronic commerce development in both countries.

Visual surveillance systems are increasingly common in our society today. There is a conflict between the demands for security of the public and the demands to preserve the personal integrity. This thesis suggests a solution in which parts of the surveillance images are covered in order to conceal the identities of persons appearing in video, but not their actions or activities. The covered parts could be encrypted and unlocked only by the police or another legal authority in case of a crime. This thesis implements a proof-of-concept demonstrator using a combination of image processing techniques such as foreground segmentation, mathematical morphology, geometric camera calibration and region tracking. The demonstrator is capable of tracking a moderate number of moving objects and conceal their identity by replacing them with a mask or a blurred image. Functionality for replaying recorded data and unlocking individual persons are included. The concept demonstrator shows the chain from concealing the identities of persons to unlocking only a single person on recorded data. Evaluation on a publicly available dataset shows overall good performance.

As data mining matures as a field and develops more powerful algorithms for discovering and exploiting patterns in data, the amount of data about individuals that is collected and stored continues to rapidly increase. This increase in data heightens concerns that data mining violates individual privacy. The goal of data mining is to derive aggregate conclusions, which should not reveal sensitive information. However, the data-mining algorithms run on databases containing information about individuals which may be sensitive. The goal of privacy-preserving data mining is to provide high-quality aggregate conclusions while protecting the privacy of the constituent individuals. The field of "privacy-preserving data mining" encompasses a wide variety of different techniques and approaches, and considers many different threat and trust models. Some techniques use perturbation, where noise is added (either directly to the database that is the input to the algorithm or to the output of queries) to obscure values of sensitive attributes; some use generalization, where identifying attributes are given less specific values; and some use cryp- tography, where joint computations between multiple parties are performed on encrypted data to hide inputs. Because these approaches are applied to different scenarios with different threat models, their overall e ectiveness and privacy properties are incomparable. In this thesis I take a pragmatic approach to privacy-preserving data mining and attempt to determine which techniques are suitable to real-world problems that a data miner might wish to solve, such as evaluating and learning decision-tree classifiers. I show that popular techniques for sanitizing databases prior to publication either fail to provide any meaningful privacy guarantees, or else degrade the data to the point of having only negligible data-mining utility. Cryptographic techniques for secure multi-party computation are a natural alternative to sanitized data publication, and guarantee the privacy of inputs by performing computations on encrypted data. Because of its heavy reliance on public-key cryptography, it is conventionally thought to be too slow to apply to real-world problems. I show that tailor-made protocols for specific data-mining problems can be made fast enough to run on real-world problems, and I strengthen this claim with empirical runtime analysis using prototype implementations. I also expand the use of secure computation beyond its traditional scope of applying a known algorithm to private inputs by showing how it can be used to e ciently apply a private algorithm, chosen from a specific class of algorithms, to a private input. / text