PRIVACY PRESERVING DATA MINING FOR NUMERICAL MATRICES, SOCIAL NETWORKS, AND BIG DATA

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

Security in Data Mining- A Comprehensive Survey

Data mining techniques, while allowing the individuals to extract hidden knowledge on one hand, introduce a number of privacy threats on the other hand. In this paper, we study some of these issues along with a detailed discussion on the applications of various data mining techniques for providing security. An efficient classification technique when used properly, would allow an user to differentiate between a phishing website and a normal website, to classify the users as normal users and criminals based on their activities on Social networks (Crime Profiling) and to prevent users from executing malicious codes by labelling them as malicious. The most important applications of Data mining is the detection of intrusions, where different Data mining techniques can be applied to effectively detect an intrusion and report in real time so that necessary actions are taken to thwart the attempts of the intruder. Privacy Preservation, Outlier Detection, Anomaly Detection and PhishingWebsite Classification are discussed in this paper

Privacy by Design in Data Mining

Privacy is ever-growing concern in our society: the lack of reliable privacy safeguards in many current services and devices is the basis of a diffusion that is often more limited than expected. Moreover, people feel reluctant to provide true personal data, unless it is absolutely necessary. Thus, privacy is becoming a fundamental aspect to take into account when one wants to use, publish and analyze data involving sensitive information. Many recent research works have focused on the study of privacy protection: some of these studies aim at individual privacy, i.e., the protection of sensitive individual data, while others aim at corporate privacy, i.e., the protection of strategic information at organization level. Unfortunately, it is in- creasingly hard to transform the data in a way that it protects sensitive information: we live in the era of big data characterized by unprecedented opportunities to sense, store and analyze complex data which describes human activities in great detail and resolution. As a result anonymization simply cannot be accomplished by de-identification. In the last few years, several techniques for creating anonymous or obfuscated versions of data sets have been proposed, which essentially aim to find an acceptable trade-off between data privacy on the one hand and data utility on the other. So far, the common result obtained is that no general method exists which is capable of both dealing with “generic personal data” and preserving “generic analytical results”. In this thesis we propose the design of technological frameworks to counter the threats of undesirable, unlawful effects of privacy violation, without obstructing the knowledge discovery opportunities of data mining technologies. Our main idea is to inscribe privacy protection into the knowledge discovery technol- ogy by design, so that the analysis incorporates the relevant privacy requirements from the start. Therefore, we propose the privacy-by-design paradigm that sheds a new light on the study of privacy protection: once specific assumptions are made about the sensitive data and the target mining queries that are to be answered with the data, it is conceivable to design a framework to: a) transform the source data into an anonymous version with a quantifiable privacy guarantee, and b) guarantee that the target mining queries can be answered correctly using the transformed data instead of the original ones. This thesis investigates on two new research issues which arise in modern Data Mining and Data Privacy: individual privacy protection in data publishing while preserving specific data mining analysis, and corporate privacy protection in data mining outsourcing

Privacy Preserving Data Mining, Evaluation Methodologies

Privacy is one of the most important properties an information system must satisfy. A relatively new trend shows that classical access control techniques are not sufficient to guarantee privacy when datamining techniques are used. Privacy Preserving Data Mining (PPDM) algorithms have been recently introduced with the aim of modifying the database in such a way to prevent the discovery of sensible information. Due to the large amount of possible techniques that can be used to achieve this goal, it is necessary to provide some standard evaluation metrics to determine the best algorithms for a specific application or context. Currently, however, there is no common set of parameters that can be used for this purpose. Moreover, because sanitization modifies the data, an important issue, especially for critical data, is to preserve the quality of data. However, to the best of our knowledge, no approaches have been developed dealing with the issue of data quality in the context of PPDM algorithms. This report explores the problem of PPDM algorithm evaluation, starting from the key goal of preserving of data quality. To achieve such goal, we propose a formal definition of data quality specifically tailored for use in the context of PPDM algorithms, a set of evaluation parameters and an evaluation algorithm. Moreover, because of the "environment related" nature of data quality, a structure to represent constraints and information relevance related to data is presented. The resulting evaluation core process is then presented as a part of a more general three step evaluation framework, taking also into account other aspects of the algorithm evaluation such as efficiency, scalability and level of privacy.JRC.G.6-Sensors, radar technologies and cybersecurit

Private Graph Data Release: A Survey

The application of graph analytics to various domains have yielded tremendous societal and economical benefits in recent years. However, the increasingly widespread adoption of graph analytics comes with a commensurate increase in the need to protect private information in graph databases, especially in light of the many privacy breaches in real-world graph data that was supposed to preserve sensitive information. This paper provides a comprehensive survey of private graph data release algorithms that seek to achieve the fine balance between privacy and utility, with a specific focus on provably private mechanisms. Many of these mechanisms fall under natural extensions of the Differential Privacy framework to graph data, but we also investigate more general privacy formulations like Pufferfish Privacy that can deal with the limitations of Differential Privacy. A wide-ranging survey of the applications of private graph data release mechanisms to social networks, finance, supply chain, health and energy is also provided. This survey paper and the taxonomy it provides should benefit practitioners and researchers alike in the increasingly important area of private graph data release and analysis