Improved k-Anonymize and l-Diverse Approach for Privacy Preserving Big Data Publishing Using MPSEC Dataset

Data exposure and privacy violations may happen when data is exchanged between organizations. Data anonymization gives promising results for limiting such dangers. In order to maintain privacy, different methods of k-anonymization and l-diversity have been widely used. But for larger datasets, the results are not very promising. The main problem with existing anonymization algorithms is high information loss and high running time. To overcome this problem, this paper proposes new models, namely Improved k-Anonymization (IKA) and Improved l-Diversity (ILD). IKA model takes large k-value using a symmetric as well as an asymmetric anonymizing algorithm. Then IKA is further categorized into Improved Symmetric k-Anonymization (ISKA) and Improved Asymmetric k-Anonymization (IAKA). After anonymizing data using IKA, ILD model is used to increase privacy. ILD will make the data more diverse and thereby increasing privacy. This paper presents the implementation of the proposed IKA and ILD model using real-time big candidate election dataset, which is acquired from the Madhya Pradesh State Election Commission, India (MPSEC) along with Apache Storm. This paper also compares the proposed model with existing algorithms, i.e. Fast clustering-based Anonymization for Data Streams (FADS), Fast Anonymization for Data Stream (FAST), Map Reduce Anonymization (MRA) and Scalable k-Anonymization (SKA). The experimental results show that the proposed models IKA and ILD have remarkable improvement of information loss and significantly enhanced the performance in terms of running time over the existing approaches along with maintaining the privacy-utility trade-off

Garantia de privacidade na exploração de bases de dados distribuídas

Anonymisation is currently one of the biggest challenges when sharing sensitive personal information. Its importance depends largely on the application domain, but when dealing with health information, this becomes a more serious issue. A simpler approach to avoid this disclosure is to ensure that all data that can be associated directly with an individual is removed from the original dataset. However, some studies have shown that simple anonymisation procedures can sometimes be reverted using specific patients’ characteristics, namely when the anonymisation is based on hidden key attributes. In this work, we propose a secure architecture to share information from distributed databases without compromising the subjects’ privacy. The work was initially focused on identifying techniques to link information between multiple data sources, in order to revert the anonymization procedures. In a second phase, we developed the methodology to perform queries over distributed databases was proposed. The architecture was validated using a standard data schema that is widely adopted in observational research studies.A garantia da anonimização de dados é atualmente um dos maiores desafios quando existe a necessidade de partilhar informações pessoais de carácter sensível. Apesar de ser um problema transversal a muitos domínios de aplicação, este torna-se mais crítico quando a anonimização envolve dados clinicos. Nestes casos, a abordagem mais comum para evitar a divulgação de dados, que possam ser associados diretamente a um indivíduo, consiste na remoção de atributos identificadores. No entanto, segundo a literatura, esta abordagem não oferece uma garantia total de anonimato, que pode ser quebrada através de ataques específicos que permitem a reidentificação dos sujeitos. Neste trabalho, é proposta uma arquitetura que permite partilhar dados armazenados em repositórios distribuídos, de forma segura e sem comprometer a privacidade. Numa primeira fase deste trabalho, foi feita uma análise de técnicas que permitam reverter os procedimentos de anonimização. Na fase seguinte, foi proposta uma metodologia que permite realizar pesquisas em bases de dados distribuídas, sem que o anonimato seja quebrado. Esta arquitetura foi validada sobre um esquema de base de dados relacional que é amplamente utilizado em estudos clínicos observacionais.Mestrado em Ciberseguranç

Cloud based privacy preserving data mining model using hybrid k-anonymity and partial homomorphic encryption

The evolution of information and communication technologies have encourage numerous organizations to outsource their business and data to cloud computing to perform data mining and other data processing operations. Despite the great benefits of the cloud, it has a real problem in the security and privacy of data. Many studies explained that attackers often reveal the information from third-party services or third-party clouds. When a data owners outsource their data to the cloud, especially the SaaS cloud model, it is difficult to preserve the confidentiality and integrity of the data. Privacy-Preserving Data Mining (PPDM) aims to accomplish data mining operations while protecting the owner's data from violation. The current models of PPDM have some limitations. That is, they suffer from data disclosure caused by identity and attributes disclosure where some private information is revealed which causes the success of different types of attacks. Besides, existing solutions have poor data utility and high computational performance overhead. Therefore, this research aims to design and develop Hybrid Anonymization Cryptography PPDM (HAC-PPDM) model to improve the privacy-preserving level by reducing data disclosure before outsourcing data for mining over the cloud while maintaining data utility. The proposed HAC-PPDM model is further aimed reducing the computational performance overhead to improve efficiency. The Quasi-Identifiers Recognition algorithm (QIR) is defined and designed depending on attributes classification and Quasi-Identifiers dimension determine to overcome the identity disclosure caused by Quasi-Identifiers linking to reduce privacy leakage. An Enhanced Homomorphic Scheme is designed based on hybridizing Cloud-RSA encryption scheme, Extended Euclidean algorithm (EE), Fast Modular Exponentiation algorithm (FME), and Chinese Remainder Theorem (CRT) to minimize the computational time complexity while reducing the attribute disclosure. The proposed QIR, Enhanced Homomorphic Scheme and k-anonymity privacy model have been hybridized to obtain optimal data privacy-preservation before outsourced it on the cloud while maintaining the utility of data that meets the needs of mining with good efficiency. Real-world datasets have been used to evaluate the proposed algorithms and model. The experimental results show that the proposed QIR algorithm improved the data privacy-preserving percentage by 23% while maintaining the same or slightly better data utility. Meanwhile, the proposed Enhanced Homomorphic Scheme is more efficient comparing to the related works in terms of time complexity as represented by Big O notation. Moreover, it reduced the computational time of the encryption, decryption, and key generation time. Finally, the proposed HAC-PPDM model successfully reduced the data disclosures and improved the privacy-preserving level while preserved the data utility as it reduced the information loss. In short, it achieved improvement of privacy preserving and data mining (classification) accuracy by 7.59 % and 0.11 % respectively

Contribution to privacy-enhancing tecnologies for machine learning applications

For some time now, big data applications have been enabling revolutionary innovation in every aspect of our daily life by taking advantage of lots of data generated from the interactions of users with technology. Supported by machine learning and unprecedented computation capabilities, different entities are capable of efficiently exploiting such data to obtain significant utility. However, since personal information is involved, these practices raise serious privacy concerns. Although multiple privacy protection mechanisms have been proposed, there are some challenges that need to be addressed for these mechanisms to be adopted in practice, i.e., to be “usable” beyond the privacy guarantee offered. To start, the real impact of privacy protection mechanisms on data utility is not clear, thus an empirical evaluation of such impact is crucial. Moreover, since privacy is commonly obtained through the perturbation of large data sets, usable privacy technologies may require not only preservation of data utility but also efficient algorithms in terms of computation speed. Satisfying both requirements is key to encourage the adoption of privacy initiatives. Although considerable effort has been devoted to design less “destructive” privacy mechanisms, the utility metrics employed may not be appropriate, thus the wellness of such mechanisms would be incorrectly measured. On the other hand, despite the advent of big data, more efficient approaches are not being considered. Not complying with the requirements of current applications may hinder the adoption of privacy technologies. In the first part of this thesis, we address the problem of measuring the effect of k-anonymous microaggregation on the empirical utility of microdata. We quantify utility accordingly as the accuracy of classification models learned from microaggregated data, evaluated over original test data. Our experiments show that the impact of the de facto microaggregation standard on the performance of machine-learning algorithms is often minor for a variety of data sets. Furthermore, experimental evidence suggests that the traditional measure of distortion in the community of microdata anonymization may be inappropriate for evaluating the utility of microaggregated data. Secondly, we address the problem of preserving the empirical utility of data. By transforming the original data records to a different data space, our approach, based on linear discriminant analysis, enables k-anonymous microaggregation to be adapted to the application domain of data. To do this, first, data is rotated (projected) towards the direction of maximum discrimination and, second, scaled in this direction, penalizing distortion across the classification threshold. As a result, data utility is preserved in terms of the accuracy of machine learned models for a number of standardized data sets. Afterwards, we propose a mechanism to reduce the running time for the k-anonymous microaggregation algorithm. This is obtained by simplifying the internal operations of the original algorithm. Through extensive experimentation over multiple data sets, we show that the new algorithm gets significantly faster. Interestingly, this remarkable speedup factor is achieved with no additional loss of data utility.Les aplicacions de big data impulsen actualment una accelerada innovació aprofitant la gran quantitat d’informació generada a partir de les interaccions dels usuaris amb la tecnologia. Així, qualsevol entitat és capaç d'explotar eficientment les dades per obtenir utilitat, emprant aprenentatge automàtic i capacitats de còmput sense precedents. No obstant això, sorgeixen en aquest escenari serioses preocupacions pel que fa a la privacitat dels usuaris ja que hi ha informació personal involucrada. Tot i que s'han proposat diversos mecanismes de protecció, hi ha alguns reptes per a la seva adopció en la pràctica, és a dir perquè es puguin utilitzar. Per començar, l’impacte real d'aquests mecanismes en la utilitat de les dades no esta clar, raó per la qual la seva avaluació empírica és important. A més, considerant que actualment es manegen grans volums de dades, una privacitat usable requereix, no només preservació de la utilitat de les dades, sinó també algoritmes eficients en temes de temps de còmput. És clau satisfer tots dos requeriments per incentivar l’adopció de mesures de privacitat. Malgrat que hi ha diversos esforços per dissenyar mecanismes de privacitat menys "destructius", les mètriques d'utilitat emprades no serien apropiades, de manera que aquests mecanismes de protecció podrien estar sent incorrectament avaluats. D'altra banda, tot i l’adveniment del big data, la investigació existent no s’enfoca molt en millorar la seva eficiència. Lamentablement, si els requisits de les aplicacions actuals no es satisfan, s’obstaculitzarà l'adopció de tecnologies de privacitat. A la primera part d'aquesta tesi abordem el problema de mesurar l'impacte de la microagregació k-Gnónima en la utilitat empírica de microdades. Per això, quantifiquem la utilitat com la precisió de models de classificació obtinguts a partir de les dades microagregades. i avaluats sobre dades de prova originals. Els experiments mostren que l'impacte de l’algoritme de rmicroagregació estàndard en el rendiment d’algoritmes d'aprenentatge automàtic és usualment menor per a una varietat de conjunts de dades avaluats. A més, l’evidència experimental suggereix que la mètrica tradicional de distorsió de les dades seria inapropiada per avaluar la utilitat empírica de dades microagregades. Així també estudiem el problema de preservar la utilitat empírica de les dades a l'ésser anonimitzades. Transformant els registres originaIs de dades en un espai de dades diferent, el nostre enfocament, basat en anàlisi de discriminant lineal, permet que el procés de microagregació k-anònima s'adapti al domini d’aplicació de les dades. Per això, primer, les dades són rotades o projectades en la direcció de màxima discriminació i, segon, escalades en aquesta direcció, penalitzant la distorsió a través del llindar de classificació. Com a resultat, la utilitat de les dades es preserva en termes de la precisió dels models d'aprenentatge automàtic en diversos conjunts de dades. Posteriorment, proposem un mecanisme per reduir el temps d'execució per a la microagregació k-anònima. Això s'aconsegueix simplificant les operacions internes de l'algoritme escollit Mitjançant una extensa experimentació sobre diversos conjunts de dades, vam mostrar que el nou algoritme és bastant més ràpid. Aquesta acceleració s'aconsegueix sense que hi ha pèrdua en la utilitat de les dades. Finalment, en un enfocament més aplicat, es proposa una eina de protecció de privacitat d'individus i organitzacions mitjançant l'anonimització de dades sensibles inclosos en logs de seguretat. Es dissenyen diferents mecanismes d'anonimat per implementar-los en base a la definició d'una política de privacitat, en el context d'un projecte europeu que té per objectiu construir un sistema de seguretat unificat

Contribution to privacy-enhancing tecnologies for machine learning applications

For some time now, big data applications have been enabling revolutionary innovation in every aspect of our daily life by taking advantage of lots of data generated from the interactions of users with technology. Supported by machine learning and unprecedented computation capabilities, different entities are capable of efficiently exploiting such data to obtain significant utility. However, since personal information is involved, these practices raise serious privacy concerns. Although multiple privacy protection mechanisms have been proposed, there are some challenges that need to be addressed for these mechanisms to be adopted in practice, i.e., to be “usable” beyond the privacy guarantee offered. To start, the real impact of privacy protection mechanisms on data utility is not clear, thus an empirical evaluation of such impact is crucial. Moreover, since privacy is commonly obtained through the perturbation of large data sets, usable privacy technologies may require not only preservation of data utility but also efficient algorithms in terms of computation speed. Satisfying both requirements is key to encourage the adoption of privacy initiatives. Although considerable effort has been devoted to design less “destructive” privacy mechanisms, the utility metrics employed may not be appropriate, thus the wellness of such mechanisms would be incorrectly measured. On the other hand, despite the advent of big data, more efficient approaches are not being considered. Not complying with the requirements of current applications may hinder the adoption of privacy technologies. In the first part of this thesis, we address the problem of measuring the effect of k-anonymous microaggregation on the empirical utility of microdata. We quantify utility accordingly as the accuracy of classification models learned from microaggregated data, evaluated over original test data. Our experiments show that the impact of the de facto microaggregation standard on the performance of machine-learning algorithms is often minor for a variety of data sets. Furthermore, experimental evidence suggests that the traditional measure of distortion in the community of microdata anonymization may be inappropriate for evaluating the utility of microaggregated data. Secondly, we address the problem of preserving the empirical utility of data. By transforming the original data records to a different data space, our approach, based on linear discriminant analysis, enables k-anonymous microaggregation to be adapted to the application domain of data. To do this, first, data is rotated (projected) towards the direction of maximum discrimination and, second, scaled in this direction, penalizing distortion across the classification threshold. As a result, data utility is preserved in terms of the accuracy of machine learned models for a number of standardized data sets. Afterwards, we propose a mechanism to reduce the running time for the k-anonymous microaggregation algorithm. This is obtained by simplifying the internal operations of the original algorithm. Through extensive experimentation over multiple data sets, we show that the new algorithm gets significantly faster. Interestingly, this remarkable speedup factor is achieved with no additional loss of data utility.Les aplicacions de big data impulsen actualment una accelerada innovació aprofitant la gran quantitat d’informació generada a partir de les interaccions dels usuaris amb la tecnologia. Així, qualsevol entitat és capaç d'explotar eficientment les dades per obtenir utilitat, emprant aprenentatge automàtic i capacitats de còmput sense precedents. No obstant això, sorgeixen en aquest escenari serioses preocupacions pel que fa a la privacitat dels usuaris ja que hi ha informació personal involucrada. Tot i que s'han proposat diversos mecanismes de protecció, hi ha alguns reptes per a la seva adopció en la pràctica, és a dir perquè es puguin utilitzar. Per començar, l’impacte real d'aquests mecanismes en la utilitat de les dades no esta clar, raó per la qual la seva avaluació empírica és important. A més, considerant que actualment es manegen grans volums de dades, una privacitat usable requereix, no només preservació de la utilitat de les dades, sinó també algoritmes eficients en temes de temps de còmput. És clau satisfer tots dos requeriments per incentivar l’adopció de mesures de privacitat. Malgrat que hi ha diversos esforços per dissenyar mecanismes de privacitat menys "destructius", les mètriques d'utilitat emprades no serien apropiades, de manera que aquests mecanismes de protecció podrien estar sent incorrectament avaluats. D'altra banda, tot i l’adveniment del big data, la investigació existent no s’enfoca molt en millorar la seva eficiència. Lamentablement, si els requisits de les aplicacions actuals no es satisfan, s’obstaculitzarà l'adopció de tecnologies de privacitat. A la primera part d'aquesta tesi abordem el problema de mesurar l'impacte de la microagregació k-Gnónima en la utilitat empírica de microdades. Per això, quantifiquem la utilitat com la precisió de models de classificació obtinguts a partir de les dades microagregades. i avaluats sobre dades de prova originals. Els experiments mostren que l'impacte de l’algoritme de rmicroagregació estàndard en el rendiment d’algoritmes d'aprenentatge automàtic és usualment menor per a una varietat de conjunts de dades avaluats. A més, l’evidència experimental suggereix que la mètrica tradicional de distorsió de les dades seria inapropiada per avaluar la utilitat empírica de dades microagregades. Així també estudiem el problema de preservar la utilitat empírica de les dades a l'ésser anonimitzades. Transformant els registres originaIs de dades en un espai de dades diferent, el nostre enfocament, basat en anàlisi de discriminant lineal, permet que el procés de microagregació k-anònima s'adapti al domini d’aplicació de les dades. Per això, primer, les dades són rotades o projectades en la direcció de màxima discriminació i, segon, escalades en aquesta direcció, penalitzant la distorsió a través del llindar de classificació. Com a resultat, la utilitat de les dades es preserva en termes de la precisió dels models d'aprenentatge automàtic en diversos conjunts de dades. Posteriorment, proposem un mecanisme per reduir el temps d'execució per a la microagregació k-anònima. Això s'aconsegueix simplificant les operacions internes de l'algoritme escollit Mitjançant una extensa experimentació sobre diversos conjunts de dades, vam mostrar que el nou algoritme és bastant més ràpid. Aquesta acceleració s'aconsegueix sense que hi ha pèrdua en la utilitat de les dades. Finalment, en un enfocament més aplicat, es proposa una eina de protecció de privacitat d'individus i organitzacions mitjançant l'anonimització de dades sensibles inclosos en logs de seguretat. Es dissenyen diferents mecanismes d'anonimat per implementar-los en base a la definició d'una política de privacitat, en el context d'un projecte europeu que té per objectiu construir un sistema de seguretat unificat.Postprint (published version

Privacy protection of user profiles in personalized information systems

In recent times we are witnessing the emergence of a wide variety of information systems that tailor the information-exchange functionality to meet the specific interests of their users. Most of these personalized information systems capitalize on, or lend themselves to, the construction of profiles, either directly declared by a user, or inferred from past activity. The ability of these systems to profile users is therefore what enables such intelligent functionality, but at the same time, it is the source of serious privacy concerns. Although there exists a broad range of privacy-enhancing technologies aimed to mitigate many of those concerns, the fact is that their use is far from being widespread. The main reason is that there is a certain ambiguity about these technologies and their effectiveness in terms of privacy protection. Besides, since these technologies normally come at the expense of system functionality and utility, it is challenging to assess whether the gain in privacy compensates for the costs in utility. Assessing the privacy provided by a privacy-enhancing technology is thus crucial to determine its overall benefit, to compare its effectiveness with other technologies, and ultimately to optimize it in terms of the privacy-utility trade-off posed. Considerable effort has consequently been devoted to investigating both privacy and utility metrics. However, most of these metrics are specific to concrete systems and adversary models, and hence are difficult to generalize or translate to other contexts. Moreover, in applications involving user profiles, there are a few proposals for the evaluation of privacy, and those existing are not appropriately justified or fail to justify the choice. The first part of this thesis approaches the fundamental problem of quantifying user privacy. Firstly, we present a theoretical framework for privacy-preserving systems, endowed with a unifying view of privacy in terms of the estimation error incurred by an attacker who aims to disclose the private information that the system is designed to conceal. Our theoretical analysis shows that numerous privacy metrics emerging from a broad spectrum of applications are bijectively related to this estimation error, which permits interpreting and comparing these metrics under a common perspective. Secondly, we tackle the issue of measuring privacy in the enthralling application of personalized information systems. Specifically, we propose two information-theoretic quantities as measures of the privacy of user profiles, and justify these metrics by building on Jaynes' rationale behind entropy-maximization methods and fundamental results from the method of types and hypothesis testing. Equipped with quantifiable measures of privacy and utility, the second part of this thesis investigates privacy-enhancing, data-perturbative mechanisms and architectures for two important classes of personalized information systems. In particular, we study the elimination of tags in semantic-Web applications, and the combination of the forgery and the suppression of ratings in personalized recommendation systems. We design such mechanisms to achieve the optimal privacy-utility trade-off, in the sense of maximizing privacy for a desired utility, or vice versa. We proceed in a systematic fashion by drawing upon the methodology of multiobjective optimization. Our theoretical analysis finds a closed-form solution to the problem of optimal tag suppression, and to the problem of optimal forgery and suppression of ratings. In addition, we provide an extensive theoretical characterization of the trade-off between the contrasting aspects of privacy and utility. Experimental results in real-world applications show the effectiveness of our mechanisms in terms of privacy protection, system functionality and data utility

Factors influencing cyberbullying among young adults: Instagram case study

Cyberbullying is one of the major problems of social networking sites, which has been known to have prolonged adverse psychological effects on social network users. Cyberbullying has been discussed a lot in the literature, but little research has been done on cyberbullying and its related factors. This study seeks to examine the factors influencing cyberbullying on Instagram among young adults. Instagram was chosen as a case study for the thesis because research shows that Instagram is the most preferred social networking site among the age cohort (18–30), who are popularly referred to as young adults. An extensive review of the literature was carried out, and six constructs (Instagram Usage, Vulnerability, Peer Pressure, Anonymity, and Instagram Features) were used to examine the influence of cyberbullying among young adults on Instagram. This study draws from the theory of routine activity theory (RAT), which is grounded on the postulation that criminal acts can be easily committed by any individual who has the opportunity. The researcher reviewed the process and deployed a methodological and concept-centric approach to create a comprehensive conceptual model that included key factors. This dissertation is different from most cyberbullying research in the sense that it reviews cyberbullying behaviours from the context in which they occur rather than the intent or motivation of the perpetrator. The model allowed a holistic examination of factors that influenced cyberbullying behaviours on Instagram. Using a survey methodology, over 201 Instagram users who are also students at the University of Cape Town completed an instrument measuring factor influencing cyberbullying. The researcher deployed Smart PLS, a statistical package for the social sciences, to test for reliability, validity and to analyse the entire dataset. The study critically examined the factors that influence cyberbullying among young adults. The results of this dissertation indicated that peer pressure and online vulnerability have a strong significance in cyberbullying behaviours. Surprisingly, Instagram usage had a weak correlation with cyberbullying behaviours. This study contributes significantly to the exciting research on cyberbullying as it helps identify the factors that contribute to cyberbullying behaviours. From this research, cyberbullying interventions or solutions can be accurately developed

Implementing privacy-preserving filters in the MOA stream mining framework

[CATALÀ] S'han implementat mètodes d'SDC en quatre filtres de privacitat pel software MOA. Els algorismes han estat adaptats de solucions conegudes per habilitar el seu ús en entorns de processament de fluxos. Finalment, han estat avaluats en termes del risc de revelació i la pèrdua d'informació.[ANGLÈS] Four MOA privacy-preserving filters have been developed to implement some SDC methods. The algorithms have been adapted from well-known solutions to enable their use in streaming settings. Finally, they have been benchmarked to assess their quality in terms of disclosure risk and information loss

A biography of open source software: community participation and individuation of open source code in the context of microfinance NGOs in North Africa and the Middle East

For many, microfinance is about building inclusive financial systems to help the poor gain direct access to financial services. Hundreds of grassroots have specialised in the provision of microfinance services worldwide. Most of them are adhoc organisations, which suffer severe organisational and informational deficiencies. Over the past decades, policy makers and consortia of microfinance experts have attempted to improve their capacity building through ICTs. In particular, there is strong emphasis on open source software (OSS) initiatives, as it is commonly believed that MFIs are uniquely positioned to benefit from the advantages of openness and free access. Furthermore, OSS approaches have recently become extremely popular. The OSS gurus are convinced there is a business case for a purely open source approach, especially across international development spheres. Nonetheless, getting people to agree on what is meant by OSS remains hard to achieve. On the one hand scholarly software research shows a lack of consensus and documents stories in which the OSS meaning is negotiated locally. On the other, the growing literature on ICT-for-international development does not provide answers as research, especially in the microfinance context, presents little empirical scrutiny. This thesis therefore critically explores the OSS in the microfinance context in order to understand itslong-term development and what might be some of the implications for MFIs. Theoretically I draw on the 3rd wave of research within the field of Science and Technology Studies –studies of Expertise and Experience (SEE). I couple the software ‘biography’ approach (Pollock and Williams 2009) with concepts from Simondon’s thesis on the individuation of technical beings (1958) as an integrated framework. I also design a single case study, which is supported by an extensive and longitudinal collection of data and a three-stage approach, including the analysis of sociograms, and email content. This case provides a rich empirical setting that challenges the current understanding of the ontology of software and goes beyond the instrumental views of design, building a comprehensive framework for community participation and software sustainability in the context of the microfinance global industry