Microaggregation Sorting Framework for K-Anonymity Statistical Disclosure Control in Cloud Computing

In cloud computing, there have led to an increase in the capability to store and record personal data ( microdata ) in the cloud. In most cases, data providers have no/little control that has led to concern that the personal data may be beached. Microaggregation techniques seek to protect microdata in such a way that data can be published and mined without providing any private information that can be linked to specific individuals. An optimal microaggregation method must minimize the information loss resulting from this replacement process. The challenge is how to minimize the information loss during the microaggregation process. This paper presents a sorting framework for Statistical Disclosure Control (SDC) to protect microdata in cloud computing. It consists of two stages. In the first stage, an algorithm sorts all records in a data set in a particular way to ensure that during microaggregation very dissimilar observations are never entered into the same cluster. In the second stage a microaggregation method is used to create k -anonymous clusters while minimizing the information loss. The performance of the proposed techniques is compared against the most recent microaggregation methods. Experimental results using benchmark datasets show that the proposed algorithms perform significantly better than existing associate techniques in the literature

On Utilizing Association and Interaction Concepts for Enhancing Microaggregation in Secure Statistical Databases

This paper presents a possibly pioneering endeavor to tackle the microaggregation techniques (MATs) in secure statistical databases by resorting to the principles of associative neural networks (NNs). The prior art has improved the available solutions to the MAT by incorporating proximity information, and this approach is done by recursively reducing the size of the data set by excluding points that are farthest from the centroid and points that are closest to these farthest points. Thus, although the method is extremely effective, arguably, it uses only the proximity information while ignoring the mutual interaction between the records. In this paper, we argue that interrecord relationships can be quantified in terms of the following two entities: 1) their ldquoassociationrdquo and 2) their ldquointeraction.rdquo This case means that records that are not necessarily close to each other may still be ldquogrouped,rdquo because their mutual interaction, which is quantified by invoking transitive-closure-like operations on the latter entity, could be significant, as suggested by the theoretically sound principles of NNs. By repeatedly invoking the interrecord associations and interactions, the records are grouped into sizes of cardinality ldquok,rdquo where k is the security parameter in the algorithm. Our experimental results, which are done on artificial data and benchmark real-life data sets, demonstrate that the newly proposed method is superior to the state of the art not only based on the information loss (IL) perspective but also when it concerns a criterion that involves a combination of the IL and the disclosure risk (DR)

Microaggregation sorting framework for k-anonymity statistical disclosure control in cloud computing

In cloud computing, there have led to an increase in the capability to store and record personal data (microdata) in the cloud. In most cases, data providers have no/little control that has led to concern that the personal data may be beached. Microaggregation techniques seek to protect microdata in such a way that data can be published and mined without providing any private information that can be linked to specific individuals. An optimal microaggregation method must minimize the information loss resulting from this replacement process. The challenge is how to minimize the information loss during the microaggregation process. This paper presents a sorting framework for Statistical Disclosure Control (SDC) to protect microdata in cloud computing. It consists of two stages. In the first stage, an algorithm sorts all records in a data set in a particular way to ensure that during microaggregation very dissimilar observations are never entered into the same cluster. In the second stage a microaggregation method is used to create k-anonymous clusters while minimizing the information loss. The performance of the proposed techniques is compared against the most recent microaggregation methods. Experimental results using benchmark datasets show that the proposed algorithms perform significantly better than existing associate techniques in the literature

Hybrid microaggregation for privacy preserving data mining

k-Anonymity by microaggregation is one of the most commonly used anonymization techniques. This success is owe to the achievement of a worth of interest trade-off between information loss and identity disclosure risk. However, this method may have some drawbacks. On the disclosure limitation side, there is a lack of protection against attribute disclosure. On the data utility side, dealing with a real datasets is a challenging task to achieve. Indeed, the latter are characterized by their large number of attributes and the presence of noisy data, such that outliers or, even, data with missing values. Generating an anonymous individual data useful for data mining tasks, while decreasing the influence of noisy data is a compelling task to achieve. In this paper, we introduce a new microaggregation method, called HM-pfsom, based on fuzzy possibilistic clustering. Our proposed method operates through an hybrid manner. This means that the anonymization process is applied per block of similar data. Thus, we can help to decrease the information loss during the anonymization process. The HM-pfsom approach proposes to study the distribution of confidential attributes within each sub-dataset. Then, according to the latter distribution, the privacy parameter k is determined, in such a way to preserve the diversity of confidential attributes within the anonymized microdata. This allows to decrease the disclosure risk of confidential information

Theoretical Computer Science and Discrete Mathematics

This book includes 15 articles published in the Special Issue "Theoretical Computer Science and Discrete Mathematics" of Symmetry (ISSN 2073-8994). This Special Issue is devoted to original and significant contributions to theoretical computer science and discrete mathematics. The aim was to bring together research papers linking different areas of discrete mathematics and theoretical computer science, as well as applications of discrete mathematics to other areas of science and technology. The Special Issue covers topics in discrete mathematics including (but not limited to) graph theory, cryptography, numerical semigroups, discrete optimization, algorithms, and complexity

Improvements to Iterated Local Search for Microaggregation

Microaggregation is a disclosure control method that uses k-anonymity to protect confidentiality in microdata while seeking minimal information loss. The problem is NP-hard. Iterated local search for microaggregation (ILSM) is an effective metaheuristic algorithm that consistently identifies better quality solutions than extant microaggregation methods. The present work presents improvements to local search, the perturbation operations and acceptance criterion within ILSM. The first, ILSMC, targets changed clusters within local search (LS) to avoid vast numbers of comparison tests, significantly reducing execution times. Second, a new probability distribution yields a better perturbation operator for most cases, significantly reducing the number of iterations needed to find similar quality solutions. A third improves the acceptance criterion by replacing the static balance between intensification and diversification with a dynamic balance. This helps ILSM escape local optima more quickly for some datasets and values of k. Experimental results with benchmark data show that ILSMC consistently reduces execution times significantly. Targeting changed clusters within LS avoids vast numbers of unproductive tests while allowing search to concentrate on more productive ones. Execution times are decreased by more than an order of magnitude for most benchmark test cases. In the worst case it decreased execution times by 75%. Advantageously, the biggest improvements were with the largest datasets. Perturbing clusters with higher information loss tend to reduce information loss more. Biasing the perturbation operations toward clusters with higher information loss increases the rate of improvement by more than 50 percent in the earliest iterations for two of the benchmarks. Occasionally accepting worse solutions provides diversification; however, increasing the probability of accepting worse solutions closer in quality to the current best solution aids in escaping local optima. This increases the rate of improvement by up to 30 percent in the earliest iterations. Combining the new perturbation operation with the new acceptance criterion can further increase the rate of improvement by as much as 20 percent for some test cases. All three improvements are orthogonal and can be combined for additive effect

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