The European Union has introduced a new General Data Protection Regulation that regulates all aspects of privacy and data protection for the data of European citizens. To transition to the new rules, companies and public institutions were given two years to adapt their systems and controls. Due to the large area of changes the GDPR requires, many companies are facing severe problems to adapt the rules to be ready for enforcement. This marks the purpose of this study which is to look into compliance practices in the implementation of GDPR requirements. This includes a prospect of compliance mechanisms that may remain insufficiently addressed when the regulation comes into force on May 25, 2018. The study is conducted in Sweden and aims to investigate the situation in corporations and not in public institutions. Mixed methods have been applied by surveying and interviewing Swedish GDPR experts and consultants to gain an understanding of their view by using capability maturity scales to assess a variety of security processes and controls. The analysis shows a low implementation in GDPR requirements while having seen improvements over the past two years of transition. It points out that a holistic strategy towards compliance is mostly missing and many companies face obstacles that are difficult to overcome in a short period. This may result in non-compliance in many Swedish corporations after the regulation comes into force on May 25.

Master of Science / Computing and Information Sciences / Eugene Vasserman / The increase in penetration of Internet technology throughout the world is bringing an increasing volume of user information online, and developing countries such as those of East Africa are included as contributors and consumers of this voluminous information. While we have seen concerns from other parts of the world regarding user privacy and security, very little is known of East African Internet users’ concern with their online information exposure. The aim of this study is to compare Internet user awareness and concerns regarding online privacy and security between East Africa (EA) and the United States (U.S.) and to determine any common attitudes and differences. The study followed a quantitative research approach, with the EA population sampled from the Open University of Tanzania, an open and distance-learning university in East Africa, and the U.S. population sampled from Kansas State University, a public university in the U.S. Online questionnaires were used as survey instruments. The results show no significant difference in awareness of online privacy between Internet users from East Africa and the U.S. There is however, significant difference in concerns about online privacy, which differ with the type of information shared. Moreover, the results have shown that the U.S. Internet users are more aware of online privacy concerns, and more likely to have taken measure to protect their online privacy and conceal their online presence, than the East African Internet users. This study has also shown that East Africans Internet users are more likely to be victims of online identity theft, security issues and reputation damage.

Submitted by Alison Vanceto (alison-vanceto@hotmail.com) on 2017-05-08T12:54:39Z No. of bitstreams: 1 DissDRGP.pdf: 2915023 bytes, checksum: 6dc48dd58772bd3d2917206ca9a92646 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-05-10T14:04:50Z (GMT) No. of bitstreams: 1 DissDRGP.pdf: 2915023 bytes, checksum: 6dc48dd58772bd3d2917206ca9a92646 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-05-10T14:04:57Z (GMT) No. of bitstreams: 1 DissDRGP.pdf: 2915023 bytes, checksum: 6dc48dd58772bd3d2917206ca9a92646 (MD5) / Made available in DSpace on 2017-05-10T14:09:36Z (GMT). No. of bitstreams: 1 DissDRGP.pdf: 2915023 bytes, checksum: 6dc48dd58772bd3d2917206ca9a92646 (MD5) Previous issue date: 2016-07-13 / Não recebi financiamento / Users do not usually read privacy policies from online services. Among the main reasons for that is the fact that such policies are long and commonly hard to understand, which makes the user lose interest in reading them carefully. In this scenario, users are prone to agree to the policies terms without knowing what kind of data is being collected and why. This dissertation discusses how the policies' content may be presented in a more friendly way, showing information about data collection and usage in a table herein called Privacy Label. The Privacy Label is a table with lines named according to data collection terms and columns named according to expressions that reveal how the data is used by the service. The table content shows if the policy collects a particular data to a particular usage. To generate the Privacy Label, a study was made in a set of privacy policies to identify which terms repeat more often along the texts. To do so, we used techniques to find keywords, and from these keywords we were able to create privacy categories. The categories define which kind of data is being collected and why, which are represented by cells in the Privacy Label. Using word comparison techniques, a privacy policy can be analyzed and important information can be extracted by comparing its terms with the terms from the privacy categories. For each category we find, we show it in the Privacy Label. To assess the proposed approach we developed an application prototype, herein called PPMark, that analyzes a particular privacy policy, extract its keywords and generates the Privacy Label automatically. The information extracted was analyzed regarding its quality using three metrics: precision, recall and f-measure. The results show that the approach is a viable functional alternative to generate the Privacy Label and present privacy policies in a friendly manner. There are evidences of time saving by using our approach, which facilitates the process of decision making. / Comumente, os usuários não leem as políticas de privacidade dos serviços online que utilizam. Entre as principais causas estão os textos longos, muitas vezes de difícil compreensão, desestimulando o interesse pela leitura atenciosa e integral. Neste cenário, os usuários, muitas vezes, concordam com os termos sem saber os tipos de dados que estão sendo coletados e o porquê. Esta dissertação discute como o conteúdo das políticas de privacidade pode ser apresentado de forma mais sintética para o usuário, com as informações sobre a coleta e a utilização dos dados sendo exibidas em uma tabela, denominada Rótulo de Privacidade. O Rótulo de Privacidade é uma tabela com linhas nomeadas por termos de coleta de dados e colunas nomeadas por expressões que denotam finalidade das coletas. O conteúdo da tabela informa se a política contempla a coleta de dados para a finalidade especificada. Para ser possível a geração do Rótulo de Privacidade, foi feito um estudo em um conjunto de políticas de privacidade para verificar quais termos mais se repetem nos textos. Para isto foram utilizadas técnicas para encontrar palavras-chave e com estas foram criadas categorias de privacidade. As categorias definem tipos de dados coletados e propósitos da coleta, que no Rótulo de Privacidade são representados pelas células da tabela. Utilizando técnicas de comparação de palavras, uma política de privacidade a ser lida pelo usuário pode ser analisada pela abordagem, extraindo informações importantes por meio das comparações de seus termos com os termos das categorias de privacidade elaboradas. Para cada categoria encontrada na política de privacidade, a informação é ilustrada no Rótulo de Privacidade. Para a avaliação da abordagem proposta, foi desenvolvido um protótipo de uma aplicação, denominada PPMark, que analisa uma particular política de privacidade, extrai as palavras-chave e gera o Rótulo de Privacidade de forma automatizada. As informações extraídas foram analisadas quanto à qualidade utilizandose três métricas que são empregadas para a avaliação de classificadores, sendo elas precisão, recall e f-measure. Os resultados mostraram que a abordagem proposta é uma alternativa funcional para o preenchimento do Rótulo de Privacidade e a apresentação das políticas de privacidade. Há evidências de economia de tempo com a leitura e entendimento das políticas, possibilitando suporte para a tomada de decisões.

The right to information privacy is considered a basic human right in countries that recognise the right to privacy. South Africa, and other countries that recognise this right, offer individuals legal protections for their information privacy. Individuals, organisations and even governments in these countries often have an obligation under such laws to protect information privacy. Large organisations, for example, multinational companies and government departments are of special concern when it comes to protecting information privacy as they often hold substantial amounts of information about many individuals. The protection of information privacy, therefore, has become ever more significant as technological advances enable information privacy to be breached with increasing ease. There is, however, little research on holistic approaches to protecting information privacy in large organisations. Holistic approaches take account of both technical and non-technical factors that affect information privacy. Nontechnical factors may include the management of information privacy protection measures and other factors such as manual business processes and organisational policies. Amongst the protections that can be used by large organisations to protect information privacy is the ability to investigate incidents involving information privacy. Since large organisations typically make extensive use of information technology to store or process information, such investigations are likely to involve digital forensics. Digital forensic investigations require a certain amount of preparedness or readiness for investigations to be executed in an optimal fashion. The available literature on digital forensics and digital forensic readiness (DFR), unfortunately, does not specifically deal with the protection of information privacy, which has requirements over and above typical digital forensic investigations that are more concerned with information security breaches. The aim of this thesis, therefore, is to address the lack of research into DFR with regard to information privacy incidents. It adopts a holistic approach to DFR since many of the necessary measures are non-technical. There is, thus, an increased focus on management as opposed to specific technical issues. In addressing the lack of research into information privacy-specific DFR, the thesis provides large organisations with knowledge to better conduct digital forensic investigations into information privacy incidents. Hence, it allows for increased information privacy protection in large organisations because investigations may reveal the causes of information privacy breaches. Such breaches may then be prevented in future. The ability to conduct effective investigations also has a deterrent effect that may dissuade attempts at breaching information privacy. This thesis addresses the lack of research into information privacy-specific DFR by presenting a framework that allows large organisations to develop a digital forensic readiness capability for information privacy incidents. The framework is an idealistic representation of measures that can be taken to develop such a capability. In reality, large organisations operate within cost constraints. We therefore also contribute by showing how a cost management methodology known as time-driven activity-based costing can be used to determine the cost of DFR measures. Organisations are then able to make cost versus risk decisions when deciding which measures in the framework they wish to implement. Lastly, we introduce the concept of a digital forensics management system. The management of DFR in a large organisation can be a difficult task prone to error as it involves coordinating resources across multiple departments and organisational functions. The concept of the digital forensics management system proposed here allows management to better manage DFR by providing a central system from which information is available and control is possible. We develop an architecture for such a system and validate the architecture through a proof-of-concept prototype. / Thesis (PhD)--University of Pretoria, 2012. / Computer Science / unrestricted

Detta arbete har till syfte att studera den nya dataskyddsförordningen General Data Protection Regulation´s (GDPR) utmaningar och påverkan på både organisation samt system. Fokus i arbetet har varit på det specifika kravet privacy by design som är en del av GDPR. GDPR-förordningen kommer träda i kraft den 25 maj 2018. Eftersom GDPR är en ny förordning så har det inte skett forskning i större utsträckning i ämnet ännu. Den forskning som har skett inom området har mestadels varit inom juridiken. Detta resulterar i att ämnet är högaktuellt att undersökas eftersom ny kunskap kommer tillkomma genom detta arbete. Arbetet syftar till att undersöka hur GDPR genom sina krav påverkar en organisation och hur hänsyn tas till det specifika kravet privacy by design. Det kommer också undersökas vilka krav som ställs på teknik och funktioner. Genom att göra detta kommer kunskap tas fram om och hur en organisation förbereder sig och vad som krävs för att uppfylla kraven med GDPR. Privacy by design är en filosofi på hur inbyggd integritet kan användas för att skydda och bygga in den personliga integriteten i system. Den baseras på sju principer som skall användas för att förstå hur integritet kan skyddas. Men likt alla lösningar finns det utmaningar. Det är dessa utmaningar som arbetet skall undersöka och för att utifrån resultatet ge rekommendationer som förhoppningsvis kan användas för att få en överblick hur en organisation ligger i fas med privacy by design, som är en del av GDPR. Baserat på organisationens svar kommer rekommendationer ges för hur organisationen skulle kunna förbättra sitt arbete ytterligare. Metoden som använts till stöd för detta arbete är en fallstudie av kvalitativ art, och innefattar intervjuer med personer från en organisation inom säkerhetsbranschen som utvecklar metoder samt mjukvara för informationssäkerhetsarbete. Organisationen som har undersökts befinner sig i startgroparna för säkerställandet av GDPR och har gjort en inledande analys av läget. Fyra intervjuer har legat till grund för studien och på dessa har en innehållsanalys genomförts. Med hjälp av analysen så framträder en tydlig bild av hur arbetet kan se ut, i samband med lagförändringen ur privacy by design-perspektivet. För att ta reda på detta har en organisation som arbetar med informationssäkerhet och mjukvaruutveckling undersökts. Till arbetet utvecklades en frågeguide och en sammanfattning av principer, som är relaterade till privacy by design. Det har visat sig att organisationen som undersöktes till stora delar arbetar med privacy by design, men har ytterligare utmaningar att bemöta. Analys och diskussion av intervjuerna har resulterat i rekommendationer till organisationen angående hur de kan stärka upp sitt informationssäkerhetsarbete ytterligare. Dessutom har en frågeguide, som återfinns i bilagorna, tagits fram och denna kan användas av andra organisationer som önskar undersöka hur de ligger till i sitt arbete med GDPR:s krav på privacy by design. / The purpose of this work is to study the General Data Protection Regulation (GDPR) and what challenges and impact this regulation can have on both organization and systems. The focus of the work will be on the specific requirement “privacy by design” that is one part of GDPR. The GDPR will come into force on May 25, 2018. Since the GDPR is a new regulation, there has been little research on the subject yet. The research that has taken place in the field has mostly been in the field of law. This results in the subject being highly relevant for further studies, since this work will unravel new information. The purpose of the work is to investigate how GDPR, through its requirements, affects an organization and how to take into account the specific requirement of privacy by design. It will also be investigated which demands are made of technology and functions. By doing this, knowledge will come about if and how an organization prepares and what it takes to meet the requirements of the GDPR. Privacy by design is a philosophy of how built-in integrity can be used to protect and integrate the personal integrity of systems. It is based on seven principles that will be used to understand how integrity can be protected. But like all solutions there are challenges. These are the challenges that the work will investigate, and as a result give recommendations that hopefully can be used to get an overview of how an organization, is in phase with privacy by design, which is part of GDPR. Based on the organization's response, recommendations will be given for how the organization could further improve its work. The method used to support this work is of a qualitative nature and includes interviews with persons from an organization in the security industry that develop methods and software for information security work. The organization that has been investigated is in the pitfalls for ensuring GDPR and has conducted an initial analysis of the situation. The foundation of this study relies on four interviews, on which a content analysis was made. Through this analysis, a clear picture emerges of how the work with upcoming challenges can present itself, with the changes regarding the new law concerning privacy by design. In order to investigate this, an organisation that works with information security and software development has been scrutinized. As a part of the study, a questionnaire and a summary of the principles relevant to privacy by design, was developed. The conclusion was that the scrutinized organisation generally does work with privacy by design, but still has some challenges to face. The analysis and discussion of the interviews resulted in recommendations for the organization on how to further strengthen their work with information security. Furthermore, a questionnaire, which can be found in the appendix, has been developed, and can be used by other organizations wishing to examine their progress on the work with implementing the GDPR requirements regarding privacy by design.

The Internet of Things (IoT) has been acknowledged as one of the most innovative forms of technology since the computer, because of the influence it can have on multiple sectors of physical and virtual environments. The growth of IoT is expected to continue, by 2020 the number of connected devices is estimated to reach 50 billion. Recent developments in IoT provide an unprecedented opportunity for personalised services and other benefits. To exploit these potential benefits as best as possible, individuals are willing to provide their personal information despite potential privacy breaches. Therefore, this paper examines factors that influence the willingness to disclose personal information in the use of IoT in South Africa (SA) with the use of the privacy calculus as the theoretical underpinnings of this research. The privacy calculus accentuates that a risk-benefit trade off occurs when an individual decides to disclose their personal information, however, it is assumed that there are more factors than perceived risks and perceived benefits that influence information disclosure. After analysing previous literature, this study identified the following factors; information sensitivity, privacy concerns, social influence, perceived benefits, (perceived) privacy risks and privacy knowledge as possible key tenants in relation to willingness to disclose personal information. This research took on an objective ontological view, with the underlying epistemological stance being positivistic. The research incorporated a deductive approach, employing the use of a conceptual model which was constructed from a combination of studies orientated around privacy, the privacy calculus and the privacy paradox. Data for this research was collected using the quantitative research approach, through the use of an anonymous online questionnaire, where the targeted population was narrowed down to the general public residing within SA that make use of IoT devices and/or services. Data was collected using Qualtrics and analysed using SmartPLS 3. SmartPLS 3 was used to test for correlations between the factors which influence information disclosure in the use of IoT by utilising the complete bootstrapping method. A key finding was that the privacy paradox is apparent within SA, where individuals pursue enjoyment and predominantly use IoT for leisure purposes, while individuals are more likely to adopt self-withdrawal tendencies when faced with heightened privacy concerns or potential risks.

Dans les applications de traitement du langage naturel (NLP), la formation d’un modèle efficace nécessite souvent une quantité massive de données. Cependant, les données textuelles dans le monde réel sont dispersées dans différentes institutions ou appareils d’utilisateurs. Leur partage direct avec le fournisseur de services NLP entraîne d’énormes risques pour la confidentialité, car les données textuelles contiennent souvent des informations sensibles, entraînant une fuite potentielle de la confidentialité. Un moyen typique de protéger la confidentialité consiste à privatiser directement le texte brut et à tirer parti de la confidentialité différentielle (DP) pour protéger le texte à un niveau de protection de la confidentialité quantifiable. Par ailleurs, la protection des résultats de calcul intermédiaires via un mécanisme de privatisation de texte aléatoire est une autre solution disponible. Cependant, les mécanismes existants de privatisation des textes ne permettent pas d’obtenir un bon compromis entre confidentialité et utilité en raison de la difficulté intrinsèque de la protection de la confidentialité des textes. Leurs limitations incluent principalement les aspects suivants: (1) ces mécanismes qui privatisent le texte en appliquant la notion de dχ-privacy ne sont pas applicables à toutes les métriques de similarité en raison des exigences strictes; (2) ils privatisent chaque jeton (mot) dans le texte de manière égale en fournissant le même ensemble de sorties excessivement grand, ce qui entraîne une surprotection; (3) les méthodes actuelles ne peuvent garantir la confidentialité que pour une seule étape d’entraînement/ d’inférence en raison du manque de composition DP et de techniques d’amplification DP. Le manque du compromis utilité-confidentialité empêche l’adoption des mécanismes actuels de privatisation du texte dans les applications du monde réel. Dans ce mémoire, nous proposons deux méthodes à partir de perspectives différentes pour les étapes d’apprentissage et d’inférence tout en ne requérant aucune confiance de sécurité au serveur. La première approche est un mécanisme de privatisation de texte privé différentiel personnalisé (CusText) qui attribue à chaque jeton d’entrée un ensemble de sortie personnalisé pour fournir une protection de confidentialité adaptative plus avancée au niveau du jeton. Il surmonte également la limitation des métriques de similarité causée par la notion de dχ-privacy, en adaptant le mécanisme pour satisfaire ϵ-DP. En outre, nous proposons deux nouvelles stratégies de 5 privatisation de texte pour renforcer l’utilité du texte privatisé sans compromettre la confidentialité. La deuxième approche est un modèle Gaussien privé différentiel local (GauDP) qui réduit considérablement le volume de bruit calibrée sur la base d’un cadre avancé de comptabilité de confidentialité et améliore ainsi la précision du modèle en incorporant plusieurs composants. Le modèle se compose d’une couche LDP, d’algorithmes d’amplification DP de sous-échantillonnage et de sur-échantillonnage pour l’apprentissage et l’inférence, et d’algorithmes de composition DP pour l’étalonnage du bruit. Cette nouvelle solution garantit pour la première fois la confidentialité de l’ensemble des données d’entraînement/d’inférence. Pour évaluer nos mécanismes de privatisation de texte proposés, nous menons des expériences étendues sur plusieurs ensembles de données de différents types. Les résultats expérimentaux démontrent que nos mécanismes proposés peuvent atteindre un meilleur compromis confidentialité-utilité et une meilleure valeur d’application pratique que les méthodes existantes. En outre, nous menons également une série d’études d’analyse pour explorer les facteurs cruciaux de chaque composant qui pourront fournir plus d’informations sur la protection des textes et généraliser d’autres explorations pour la NLP préservant la confidentialité. / In Natural Language Processing (NLP) applications, training an effective model often requires a massive amount of data. However, text data in the real world are scattered in different institutions or user devices. Directly sharing them with the NLP service provider brings huge privacy risks, as text data often contains sensitive information, leading to potential privacy leakage. A typical way to protect privacy is to directly privatize raw text and leverage Differential Privacy (DP) to protect the text at a quantifiable privacy protection level. Besides, protecting the intermediate computation results via a randomized text privatization mechanism is another available solution. However, existing text privatization mechanisms fail to achieve a good privacy-utility trade-off due to the intrinsic difficulty of text privacy protection. The limitations of them mainly include the following aspects: (1) those mechanisms that privatize text by applying dχ-privacy notion are not applicable for all similarity metrics because of the strict requirements; (2) they privatize each token in the text equally by providing the same and excessively large output set which results in over-protection; (3) current methods can only guarantee privacy for either the training/inference step, but not both, because of the lack of DP composition and DP amplification techniques. Bad utility-privacy trade-off performance impedes the adoption of current text privatization mechanisms in real-world applications. In this thesis, we propose two methods from different perspectives for both training and inference stages while requiring no server security trust. The first approach is a Customized differentially private Text privatization mechanism (CusText) that assigns each input token a customized output set to provide more advanced adaptive privacy protection at the token-level. It also overcomes the limitation for the similarity metrics caused by dχ-privacy notion, by turning the mechanism to satisfy ϵ-DP. Furthermore, we provide two new text privatization strategies to boost the utility of privatized text without compromising privacy. The second approach is a Gaussian-based local Differentially Private (GauDP) model that significantly reduces calibrated noise power adding to the intermediate text representations based on an advanced privacy accounting framework and thus improves model accuracy by incorporating several components. The model consists of an LDP-layer, sub-sampling and up-sampling DP amplification algorithms 7 for training and inference, and DP composition algorithms for noise calibration. This novel solution guarantees privacy for both training and inference data. To evaluate our proposed text privatization mechanisms, we conduct extensive experiments on several datasets of different types. The experimental results demonstrate that our proposed mechanisms can achieve a better privacy-utility trade-off and better practical application value than the existing methods. In addition, we also carry out a series of analyses to explore the crucial factors for each component which will be able to provide more insights in text protection and generalize further explorations for privacy-preserving NLP.

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>