Dan Tavrov

Providing group anonymity using wavelet transform

Providing public access to unprotected digital data can pose a threat of unwanted disclosing the restricted information. The problem of protecting such information can be divided into two main subclasses, namely, individual and group data anonymity. By group anonymity we define protecting important data patterns, distributions, and collective features which cannot be determined through analyzing individual records only. An effective and comparatively simple way of solving group anonymity problem is doubtlessly applying wavelet transform. Its easy-to-implement, powerful enough, and might produce acceptable results if used properly. In the paper, we present a novel method of using wavelet transform for providing group anonymity; it is gained through redistributing wavelet approximation values, along with simultaneous fixing data mean value and leaving wavelet details unchanged (or proportionally altering them). Moreover, we provide a comprehensive example to illustrate the method.

Memetic Algorithm for Solving the Task of Providing Group Anonymity

Modern information technologies enable us to analyze great amounts of primary non-aggregated data. Publishing them increases threats of disclosing sensitive information. To protect information about a single person, one needs to provide individual data anonymity. Providing group data anonymity presupposes protecting intrinsic data features, properties, and distributions. Methods for providing group anonymity need to protect the underlying data distribution, and also to ensure sufficient data utility after their transformation. In our opinion, the latter task is a problem which can be solved using only exhaustive search, therefore heuristic procedures need to be developed to find suboptimal solutions.

Memetic approach to anonymizing groups that can be approximated by a fuzzy inference system

Providing group anonymity in statistical data has become a problem of practical importance. Up to this date, there has been developed a bunch of approaches to anonymizing groups, which are applicable in many real-life applications. It can be shown that sometimes using existing methods is not sufficient to guarantee group data protection, because it is possible to violate anonymity using fuzzy models of given groups in the form of fuzzy inference systems. We propose a memetic approach to providing group anonymity in such cases.

Microfiles as a Potential Source of Confidential Information Leakage

Cyber warfares, as well as conventional ones, do not only comprise direct military conflicts involving weapons like DDoS attacks. Throughout their history, intelligence and counterintelligence played a major role as well. Information sources for intelligence can be closed (obtained during espionage) or open. In this chapter, we show that such open information sources as microfiles can be considered a potentially important additional source of information during cyber warfare. We illustrate by using real data based example that ignoring issues concerning providing group anonymity can lead to leakage of confidential information. We show that it is possible to define fuzzy groups of respondents and obtain their distribution using appropriate fuzzy inference system. We conclude the chapter with discussing methods for protecting distributions of crisp as well as fuzzy groups of respondents, and illustrate them by solving the task of providing group anonymity of a fuzzy group of “respondents who can be considered military enlisted members with the high level of confidence.”

Perceptual Computer for Grading Mathematics Tests within Bilingual Education Program

In this paper, we propose an outline of a perceptual computer for grading mathematical tests written by students studying within the bilingual education program. A generic approach to implementing such a computer is proposed. Concrete implementation is described for the case of teaching mathematics in French. The perceptual computer constructed for this case is tested with real tests written by students of one of Kyiv bilingual schools. Results show that the grades obtained using words are compatible with the grades assigned using conventional numbers, which validates the use of the perceptual computer to reduce subjectivity and uncertainty for a teacher.

Two-Phase Memetic Modifying Transformation for Solving the Task of Providing Group Anonymity

Nowadays, it has become a common practice to provide public access to various kinds of primary non-aggregated statistical data. Necessary precautions ought to be taken in order to guarantee that sensitive data features are masked, and data privacy cannot be violated. In the case of protecting information about a group of people, it is important to protect intrinsic data features and distributions. To do so, it is obligatory to introduce a certain level of distortion into the dataset. The problem of minimizing this distortion is a complex optimization task, which can be successfully solved by applying appropriate heuristic procedures, e.g., memetic algorithms. The task of determining whether a particular solution masks sensitive data features is an ill-defined one and often can be solved only by expert evaluation. In the paper, we propose to apply two-phase memetic algorithm to solve such tasks of providing group anonymity, for which it is not always possible to define appropriate constraints.

Evolutionary approach to violating group anonymity using third-party data.

In the era of Big Data, it is almost impossible to completely restrict access to primary non-aggregated statistical data. However, risk of violating privacy of individual respondents and groups of respondents by analyzing primary data has not been reduced. There is a need in developing subtler methods of data protection to come to grips with these challenges. In some cases, individual and group privacy can be easily violated, because the primary data contain attributes that uniquely identify individuals and groups thereof. Removing such attributes from the dataset is a crude solution and does not guarantee complete privacy. In the field of providing individual data anonymity, this problem has been widely recognized, and various methods have been proposed to solve it. In the current work, we demonstrate that it is possible to violate group anonymity as well, even if those attributes that uniquely identify the group are removed. As it turns out, it is possible to use third-party data to build a fuzzy model of a group. Typically, such a model comes in a form of a set of fuzzy rules, which can be used to determine membership grades of respondents in the group with a level of certainty sufficient to violate group anonymity. In the work, we introduce an evolutionary computing based method to build such a model. We also discuss a memetic approach to protecting the data from group anonymity violation in this case.

Providing Group Anonymity in Social Networks

In today’s world, there are almost no borders between people. Using Internet technologies, especially social networks, people can communicate and share different information regardless of where they live or work. However, giving out any sensitive information can pose significant security threats for the owner of the information. As more privacy challenges arise, people become concerned about their security. Many social networking websites provide various types of privacy policies, but this proves to be insufficient. All existing security methods aim at gaining individual anonymity. Nevertheless, information about user groups, which could be determined inside social networks, is not protected. Still, this information might occur to be security-intensive information is present in this data set. In this chapter, the task we have set is providing group anonymity in social networks. By group anonymity we understand the property of a group of people to be indistinguishable within a particular dataset. We also propose a technique to solve the task using wavelet transforms.