Fine-grained access control is a conceptual approach to addressing database security requirements. In relational database management systems, fine-grained access control refers to access restrictions enforced at the row, column, or cell level. While a number of commercial implementations of database fine-grained access control are available, there are presently no generalized approaches to implementing fine-grained access control for relational database management systems. Fine-grained access control is potentially a good solution for database professionals and system architects charged with designing database applications that implement granular security or privacy protection features. However, in the oral tradition of the database community, fine-grained access control is spoken of as imposing significant performance penalties, and is therefore best avoided. Regardless, there are current and emerging social, legal, and economic forces that mandate the need for efficient fine-grained access control in relational database management systems. In the study undertaken, the author was able to quantify the performance costs associated with four common implementations of fine-grained access control for relational database management systems. Security benchmarking was employed as the methodology to quantify performance costs. Synthetic data from the TPC-W benchmark as well as representative data from a real-world application were utilized in the benchmarking process. A simple graph-base performance model for Fine-grained Access Control Evaluation (FACE) was developed from benchmark data collected during the study. The FACE model is intended for use in predicting throughput and response times for relational database management systems that implement fine-grained access control using one of the common fine-grained access control mechanisms - authorization views, the Hippocratic Database, label-based access control, and transparent query rewrite. The author also addresses the issue of scalability for fine-grained access control mechanisms that were evaluated in the study.

The number of opportunities for cooperative computation has exponentially been increasing with growing interaction via Internet technologies. These computations could occur between trusted partners, between partially trusted partners, or even between competitors. Most of the time, the communicating parties may not want to disclose their private data to the other principal while taking the advantage of collaboration, hence concentrating on the results rather than private and perhaps useless data values. For performing such computations, one party must know inputs from all the participants / however if none of the parties can be trusted enough to know all the inputs, privacy will become a primary concern. Hence the techniques for Secure Multiparty Computation (SMC) are quite relevant and practical to overcome such kind of privacy gaps. The subject of SMC has evolved from earlier solutions of combinational logic circuits to the recent proposals of anonymity-enabled computation. In this thesis, we put together the significant research that has been carried out on SMC. We demonstrate the concept by concentrating on a specific technique called Oblivious Polynomial Evaluation (OPE) together with concrete examples. We put critical issues, challenges and the level of adaptation achieved before the researchers. We also provide some future research opportunities based on the literature survey.

Social networking extending the social circle of people has already become an important integral part of our daily lives. As reported by ComScore, social networking sites such as Facebook and Twitter have reached 82 percent of the world's online population, representing 1.2 billion users around the world. In the meantime, fueled by the dramatic advancements of smartphones and the ubiquitous connections of Bluetooth/WiFi/3G/LTE networks, social networking further becomes available for mobile users and keeps them posted on the up-to-date worldwide news and messages from their friends and families anytime anywhere. The convergence of social networking, advanced smartphones, and stable network infrastructures brings us a pervasive and omnipotent communication platform, named mobile social network (MSN), helping us stay connected better than ever. In the MSN, multiple communication techniques help users to launch a variety of applications in multiple communication domains including single-user domain, two-user domain, user-chain domain, and user-star domain. Within different communication domains, promising mobile applications are fostered. For example, nearby friend search application can be launched in the two-user or user-chain domains to help a user find other physically-close peers who have similar interests and preferences; local service providers disseminate advertising information to nearby users in the user-star domain; and health monitoring enables users to check the physiological signals in the single-user domain. Despite the tremendous benefits brought by the MSN, it still faces many technique challenges among of which security and privacy protections are the most important ones as smartphones are vulnerable to security attacks, users easily neglect their privacy preservation, and mutual trust relationships are difficult to be established in the MSN. In this thesis, we explore the unique characteristics and study typical research issues of the MSN. We conduct our research with a focus on security and privacy preservation while considering human factors. Specifically, we consider the profile matching application in the two-user domain, the cooperative data forwarding in the user-chain domain, the trustworthy service evaluation application in the user-star domain, and the healthcare monitoring application in the single-user domain. The main contributions are, i) considering the human comparison behavior and privacy requirements, we first propose a novel family of comparison-based privacy-preserving profile matching (PPM) protocols. The proposed protocols enable two users to obtain comparison results of attribute values in their profiles, while the attribute values are not disclosed. Taking user anonymity requirement as an evaluation metric, we analyze the anonymity protection of the proposed protocols. From the analysis, we found that the more comparison results are disclosed, the less anonymity protection is achieved by the protocol. Further, we explore the pseudonym strategy and an anonymity enhancing technique where users could be self-aware of the anonymity risk level and take appropriate actions when needed; ii) considering the inherent MSN nature --- opportunistic networking, we propose a cooperative privacy-preserving data forwarding (PDF) protocol to help users forward data to other users. We indicate that privacy and effective data forwarding are two conflicting goals: the cooperative data forwarding could be severely interrupted or even disabled when the privacy preservation of users is applied, because without sharing personal information users become unrecognizable to each other and the social interactions are no longer traceable. We explore the morality model of users from classic social theory, and use game-theoretic approach to obtain the optimal data forwarding strategy. Through simulation results, we show that the proposed cooperative data strategy can achieve both the privacy preservation and the forwarding efficiency; iii) to establish the trust relationship in a distributed MSN is a challenging task. We propose a trustworthy service evaluation (TSE) system, to help users exchange their service reviews toward local vendors. However, vendors and users could be the potential attackers aiming to disrupt the TSE system. We then consider the review attacks, i.e., vendors rejecting and modifying the authentic reviews of users, and the Sybil attacks, i.e., users abusing their pseudonyms to generate fake reviews. To prevent these attacks, we explore the token technique, the aggregate signature, and the secret sharing techniques. Simulation results show the security and the effectiveness of the TSE system can be guaranteed; iv) to improve the efficiency and reliability of communications in the single-user domain, we propose a prediction-based secure and reliable routing framework (PSR). It can be integrated with any specific routing protocol to improve the latter's reliability and prevent data injection attacks during data communication. We show that the regularity of body gesture can be learned and applied by body sensors such that the route with the highest predicted link quality can always be chose for data forwarding. The security analysis and simulation results show that the PSR significantly increases routing efficiency and reliability with or without the data injection attacks.

Social networking extending the social circle of people has already become an important integral part of our daily lives. As reported by ComScore, social networking sites such as Facebook and Twitter have reached 82 percent of the world's online population, representing 1.2 billion users around the world. In the meantime, fueled by the dramatic advancements of smartphones and the ubiquitous connections of Bluetooth/WiFi/3G/LTE networks, social networking further becomes available for mobile users and keeps them posted on the up-to-date worldwide news and messages from their friends and families anytime anywhere. The convergence of social networking, advanced smartphones, and stable network infrastructures brings us a pervasive and omnipotent communication platform, named mobile social network (MSN), helping us stay connected better than ever. In the MSN, multiple communication techniques help users to launch a variety of applications in multiple communication domains including single-user domain, two-user domain, user-chain domain, and user-star domain. Within different communication domains, promising mobile applications are fostered. For example, nearby friend search application can be launched in the two-user or user-chain domains to help a user find other physically-close peers who have similar interests and preferences; local service providers disseminate advertising information to nearby users in the user-star domain; and health monitoring enables users to check the physiological signals in the single-user domain. Despite the tremendous benefits brought by the MSN, it still faces many technique challenges among of which security and privacy protections are the most important ones as smartphones are vulnerable to security attacks, users easily neglect their privacy preservation, and mutual trust relationships are difficult to be established in the MSN. In this thesis, we explore the unique characteristics and study typical research issues of the MSN. We conduct our research with a focus on security and privacy preservation while considering human factors. Specifically, we consider the profile matching application in the two-user domain, the cooperative data forwarding in the user-chain domain, the trustworthy service evaluation application in the user-star domain, and the healthcare monitoring application in the single-user domain. The main contributions are, i) considering the human comparison behavior and privacy requirements, we first propose a novel family of comparison-based privacy-preserving profile matching (PPM) protocols. The proposed protocols enable two users to obtain comparison results of attribute values in their profiles, while the attribute values are not disclosed. Taking user anonymity requirement as an evaluation metric, we analyze the anonymity protection of the proposed protocols. From the analysis, we found that the more comparison results are disclosed, the less anonymity protection is achieved by the protocol. Further, we explore the pseudonym strategy and an anonymity enhancing technique where users could be self-aware of the anonymity risk level and take appropriate actions when needed; ii) considering the inherent MSN nature --- opportunistic networking, we propose a cooperative privacy-preserving data forwarding (PDF) protocol to help users forward data to other users. We indicate that privacy and effective data forwarding are two conflicting goals: the cooperative data forwarding could be severely interrupted or even disabled when the privacy preservation of users is applied, because without sharing personal information users become unrecognizable to each other and the social interactions are no longer traceable. We explore the morality model of users from classic social theory, and use game-theoretic approach to obtain the optimal data forwarding strategy. Through simulation results, we show that the proposed cooperative data strategy can achieve both the privacy preservation and the forwarding efficiency; iii) to establish the trust relationship in a distributed MSN is a challenging task. We propose a trustworthy service evaluation (TSE) system, to help users exchange their service reviews toward local vendors. However, vendors and users could be the potential attackers aiming to disrupt the TSE system. We then consider the review attacks, i.e., vendors rejecting and modifying the authentic reviews of users, and the Sybil attacks, i.e., users abusing their pseudonyms to generate fake reviews. To prevent these attacks, we explore the token technique, the aggregate signature, and the secret sharing techniques. Simulation results show the security and the effectiveness of the TSE system can be guaranteed; iv) to improve the efficiency and reliability of communications in the single-user domain, we propose a prediction-based secure and reliable routing framework (PSR). It can be integrated with any specific routing protocol to improve the latter's reliability and prevent data injection attacks during data communication. We show that the regularity of body gesture can be learned and applied by body sensors such that the route with the highest predicted link quality can always be chose for data forwarding. The security analysis and simulation results show that the PSR significantly increases routing efficiency and reliability with or without the data injection attacks.

Motivated by increasing public awareness of possible abuse of confidential information, which is considered as a significant hindrance to the development of e-society, medical and financial markets, a privacy preserving data mining framework is presented so that data owners can carefully process data in order to preserve confidential information and guarantee information functionality within an acceptable boundary. First, among many privacy-preserving methodologies, as a group of popular techniques for achieving a balance between data utility and information privacy, a class of data perturbation methods add a noise signal, following a statistical distribution, to an original numerical matrix. With the help of analysis in eigenspace of perturbed data, the potential privacy vulnerability of a popular data perturbation is analyzed in the presence of very little information leakage in privacy-preserving databases. The vulnerability to very little data leakage is theoretically proved and experimentally illustrated. Second, in addition to numerical matrices, social networks have played a critical role in modern e-society. Security and privacy in social networks receive a lot of attention because of recent security scandals among some popular social network service providers. So, the need to protect confidential information from being disclosed motivates us to develop multiple privacy-preserving techniques for social networks. Affinities (or weights) attached to edges are private and can lead to personal security leakage. To protect privacy of social networks, several algorithms are proposed, including Gaussian perturbation, greedy algorithm, and probability random walking algorithm. They can quickly modify original data in a large-scale situation, to satisfy different privacy requirements. Third, the era of big data is approaching on the horizon in the industrial arena and academia, as the quantity of collected data is increasing in an exponential fashion. Three issues are studied in the age of big data with privacy preservation, obtaining a high confidence about accuracy of any specific differentially private queries, speedily and accurately updating a private summary of a binary stream with I/O-awareness, and launching a mutual private information retrieval for big data. All three issues are handled by two core backbones, differential privacy and the Chernoff Bound.

abstract: The presence of a rich set of embedded sensors on mobile devices has been fuelling various sensing applications regarding the activities of individuals and their surrounding environment, and these ubiquitous sensing-capable mobile devices are pushing the new paradigm of Mobile Crowd Sensing (MCS) from concept to reality. MCS aims to outsource sensing data collection to mobile users and it could revolutionize the traditional ways of sensing data collection and processing. In the meantime, cloud computing provides cloud-backed infrastructures for mobile devices to provision their capabilities with network access. With enormous computational and storage resources along with sufficient bandwidth, it functions as the hub to handle the sensing service requests from sensing service consumers and coordinate sensing task assignment among eligible mobile users to reach a desired quality of sensing service. This paper studies the problem of sensing task assignment to mobile device owners with specific spatio-temporal traits to minimize the cost and maximize the utility in MCS while adhering to QoS constraints. Greedy approaches and hybrid solutions combined with bee algorithms are explored to address the problem. Moreover, the privacy concerns arise with the widespread deployment of MCS from both the data contributors and the sensing service consumers. The uploaded sensing data, especially those tagged with spatio-temporal information, will disclose the personal information of the data contributors. In addition, the sensing service requests can reveal the personal interests of service consumers. To address the privacy issues, this paper constructs a new framework named Privacy-Preserving Mobile Crowd Sensing (PP-MCS) to leverage the sensing capabilities of ubiquitous mobile devices and cloud infrastructures. PP-MCS has a distributed architecture without relying on trusted third parties for privacy-preservation. In PP-MCS, the sensing service consumers can retrieve data without revealing the real data contributors. Besides, the individual sensing records can be compared against the aggregation result while keeping the values of sensing records unknown, and the k-nearest neighbors could be approximately identified without privacy leaks. As such, the privacy of the data contributors and the sensing service consumers can be protected to the greatest extent possible. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2016

Le principal objectif de cette thèse est d'évaluer les protocoles qui prennent en considération la protection de la vie privée et qui nécessitent seulement des métadonnées locales pour détecter les comportements malveillants sur les réseaux sociaux décentralisés. En appliquant des techniques d'analyse de réseaux sociaux qui réduisent la quantité de métadonnées sensibles, nous obtenons des résultats acceptables comparé aux techniques qui ne préservent pas la vie privée. De plus, nous prévoyons d'élaborer une série de recommandations pour construire de futurs réseaux sociaux décentralisés qui découragent cette type des comportements abusifs. / The main goal of this thesis is to evaluate privacy-preserving protocols to detect abuse in future decentralised online social platforms or microblogging services, where often limited amount of metadata is available to perform data analytics. Taking into account such data minimization, we obtain acceptable results compared to techniques of machine learning that use all metadata available. We draw a series of conclusion and recommendations that will aid in the design and development of a privacy-preserving decentralised social network that discourages abusive behavior.

This thesis contains a critical evaluation of the unrealization approach to privacy preserving data mining. We cover a fair bit of ground, making numerous contributions to the existing literature. First, we present a comprehensive and accurate analysis of the challenges posed by data mining to privacy. Second, we put the unrealization approach on firmer ground by providing proofs of previously unproven claims, using the multi-relational algebra. Third, we extend the unrealization approach to the C4.5 algorithm. Fourth, we evaluate the algorithm's space requirements on three representative data sets. Lastly, we analyse the unrealization approach against various issues identified in the first contribution. Our conclusion is that the unrealization approach to privacy preserving data mining is novel, and capable of addressing some of the major challenges posed by data mining to privacy. Unfortunately, its space and time requirements vitiate its applicability on real-world data sets.

Systémy založené na biometrickej autentizácii sa stávajú súčasťou nášho každodenného bytia. Tieto systémy však nedovoľujú používateľom priamo alebo nepriamo meniť spôsob, akým sa k ich dátam pristupuje a ako sa s nimi bude zaobchádzať ďalej v budúcnosti. Dôsledkom tohto môžu vyplynúť riziká spojené s uniknutím identity jedinca. Táto práca sa zaoberá návrhom systému, ktorý zachováva privátnosť a zároveň umožňuje autentizáciu na základe biometrických čŕt používateľov, a to za pomoci generatívnej neurónovej siete (GAN). V práci sa konkrétne uvažuje o tom, že GAN je použitá na transformáciu obrázkov tvárí napríklad na obrázky kvetov. Autentizačný systém sídliaci na serveri je v konečnom dôsledku učený rozlišovať používateľov podľa obrázkov kvetov a nie tvárí. Na základe vykonaných experimentov môžeme potvrdiť, že navrhovaná metóda je robustná voči útokom, pričom stále vykazuje kvalitatívne požiadavky kladené na štandardný autentizačný systém.

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