Contributions to privacy in web search engines

Els motors de cerca d’Internet recullen i emmagatzemen informació sobre els seus usuaris per tal d’oferir-los millors serveis. A canvi de rebre un servei personalitzat, els usuaris perden el control de les seves pròpies dades. Els registres de cerca poden revelar informació sensible de l’usuari, o fins i tot revelar la seva identitat. En aquesta tesis tractem com limitar aquests problemes de privadesa mentre mantenim suficient informació a les dades. La primera part d’aquesta tesis tracta els mètodes per prevenir la recollida d’informació per part dels motores de cerca. Ja que aquesta informació es requerida per oferir un servei precís, l’objectiu es proporcionar registres de cerca que siguin adequats per proporcionar personalització. Amb aquesta finalitat, proposem un protocol que empra una xarxa social per tal d’ofuscar els perfils dels usuaris. La segona part tracta la disseminació de registres de cerca. Proposem tècniques que la permeten, proporcionant k-anonimat i minimitzant la pèrdua d’informació.Web Search Engines collects and stores information about their users in order to tailor their services better to their users' needs. Nevertheless, while receiving a personalized attention, the users lose the control over their own data. Search logs can disclose sensitive information and the identities of the users, creating risks of privacy breaches. In this thesis we discuss the problem of limiting the disclosure risks while minimizing the information loss. The first part of this thesis focuses on the methods to prevent the gathering of information by WSEs. Since search logs are needed in order to receive an accurate service, the aim is to provide logs that are still suitable to provide personalization. We propose a protocol which uses a social network to obfuscate users' profiles. The second part deals with the dissemination of search logs. We propose microaggregation techniques which allow the publication of search logs, providing $k$-anonymity while minimizing the information loss

Privacy-Preserving Reengineering of Model-View-Controller Application Architectures Using Linked Data

When a legacy system’s software architecture cannot be redesigned, implementing additional privacy requirements is often complex, unreliable and costly to maintain. This paper presents a privacy-by-design approach to reengineer web applications as linked data-enabled and implement access control and privacy preservation properties. The method is based on the knowledge of the application architecture, which for the Web of data is commonly designed on the basis of a model-view-controller pattern. Whereas wrapping techniques commonly used to link data of web applications duplicate the security source code, the new approach allows for the controlled disclosure of an application’s data, while preserving non-functional properties such as privacy preservation. The solution has been implemented and compared with existing linked data frameworks in terms of reliability, maintainability and complexity

Semantic microaggregation for the anonymization of query logs using the open directory project

Web search engines gather information from the queries performed by the user in the form of query logs. These logs are extremely useful for research, marketing, or profiling, but at the same time they are a great threat to the user’s privacy. We provide a novel approach to anonymize query logs so they ensure user k-anonymity, by extending a common method used in statistical disclosure control: microaggregation. Furthermore, our microaggregation approach takes into account the semantics of the queries by relying on the Open Directory Project. We have tested our proposal with real data from AOL query logsPeer Reviewe

Spherical microaggregation : anonymizing sparse vector spaces

Unstructured texts are a very popular data type and still widely unexplored in the privacy preserving data mining field. We consider the problem of providing public information about a set of confidential documents. To that end we have developed a method to protect a Vector Space Model (VSM), to make it public even if the documents it represents are private. This method is inspired by microaggregation, a popular protection method from statistical disclosure control, and adapted to work with sparse and high dimensional data sets

p-probabilistic k-anonymous microaggregation for the anonymization of surveys with uncertain participation

We develop a probabilistic variant of k-anonymous microaggregation which we term p-probabilistic resorting to a statistical model of respondent participation in order to aggregate quasi-identifiers in such a manner that k-anonymity is concordantly enforced with a parametric probabilistic guarantee. Succinctly owing the possibility that some respondents may not finally participate, sufficiently larger cells are created striving to satisfy k-anonymity with probability at least p. The microaggregation function is designed before the respondents submit their confidential data. More precisely, a specification of the function is sent to them which they may verify and apply to their quasi-identifying demographic variables prior to submitting the microaggregated data along with the confidential attributes to an authorized repository. We propose a number of metrics to assess the performance of our probabilistic approach in terms of anonymity and distortion which we proceed to investigate theoretically in depth and empirically with synthetic and standardized data. We stress that in addition to constituting a functional extension of traditional microaggregation, thereby broadening its applicability to the anonymization of statistical databases in a wide variety of contexts, the relaxation of trust assumptions is arguably expected to have a considerable impact on user acceptance and ultimately on data utility through mere availability.Peer ReviewedPostprint (author's final draft

Does k-anonymous microaggregation affect machine-learned macrotrends?

n the era of big data, the availability of massive amounts of information makes privacy protection more necessary than ever. Among a variety of anonymization mechanisms, microaggregation is a common approach to satisfy the popular requirement of k-anonymity in statistical databases. In essence, k-anonymous microaggregation aggregates quasi-identifiers to hide the identity of each data subject within a group of other k - 1 subjects. As any perturbative mechanism, however, anonymization comes at the cost of some information loss that may hinder the ulterior purpose of the released data, which very often is building machine-learning models for macrotrends analysis. To assess the impact of microaggregation on the utility of the anonymized data, it is necessary to evaluate the resulting accuracy of said models. In this paper, 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, and evaluated over original test data. Our experiments indicate, with some consistency, that the impact of the de facto microaggregation standard (maximum distance to average vector) on the performance of machine-learning algorithms is often minor to negligible for a wide range of k for a variety of classification algorithms and data sets. Furthermore, experimental evidences suggest that the traditional measure of distortion in the community of microdata anonymization may be inappropriate for evaluating the utility of microaggregated data.Postprint (published version

Exploratory study to explore the role of ICT in the process of knowledge management in an Indian business environment

In the 21st century and the emergence of a digital economy, knowledge and the knowledge base economy are rapidly growing. To effectively be able to understand the processes involved in the creating, managing and sharing of knowledge management in the business environment is critical to the success of an organization. This study builds on the previous research of the authors on the enablers of knowledge management by identifying the relationship between the enablers of knowledge management and the role played by information communication technologies (ICT) and ICT infrastructure in a business setting. This paper provides the findings of a survey collected from the four major Indian cities (Chennai, Coimbatore, Madurai and Villupuram) regarding their views and opinions about the enablers of knowledge management in business setting. A total of 80 organizations participated in the study with 100 participants in each city. The results show that ICT and ICT infrastructure can play a critical role in the creating, managing and sharing of knowledge in an Indian business environment

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