There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

자연주의적 탐구조사(Naturalistic Inquiry)

Data Mining - Concepts and Techniques.

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining Practical Machine Learning Tools and Techniques

Privacy preserving data mining has been recently introduced to cope with privacy issues related to the data subjects in the course of the mining of the data. It has also been recognized that it is not only the data that need to be protected but sensitive knowledge hidden in the data as well. Knowledge hiding is an emerging area of research focusing on appropriately modifying the data in such a way that sensitive knowledge escapes the mining and is not communicated to the public for privacy purposes. This article investigates the development of techniques falling under the knowledge-hiding umbrella that pertain to the association rule-mining task. These techniques are known as association rule hiding or frequent pattern hiding approaches, and have been receiving a lot of attention lately because they touch upon important issues of handling a sort of commonly used patterns such as the frequent patterns and the association rules. We present an overview of this area as well as a taxonomy and a presentation of an important sample of algorithms. © 2013 Wiley Periodicals, Inc.

Association Rule Hiding Methods.

In this paper, we propose a privacy model that offers trajectory privacy to the requesters of Location-Based Services (LBSs), by utilizing an underlying network of user movement. The privacy model has been implemented as a framework that (i) reconstructs the user movement from a series of independent location updates, (ii) identifies routes where user privacy is at risk, and (iii) anonymizes online user requests for LBSs to protect the requester for as long as the service withstands completion. In order to achieve (iii), we propose two anonymization techniques, the K–present (weak) and the K–frequent (strong) trajectory anonymity, and a second chance approach that takes over when anonymization fails to ensure that the privacy of the user is preserved. To the best of our knowledge, this is the first work to propose a trajectory privacy model that utilizes an underlying network of user movement to offer in an interactive way personalized privacy to online user requests on trajectory data.

https://epubs.siam.org/doi/pdf/10.1137/1.9781611972795.81

Identifying unsafe routes for network-based trajectory privacy

This paper focuses on privacy preservation in classification rule mining. The subject at hand is approached through the proposition of a data perturbation approach for hiding sensitive classification rules in categorical datasets. Such a methodology is absolutely necessary in case the data needs to be published on the web so that it is amply available for public use as opposed to other approaches like output perturbation or cryptographic techniques that restrict the usability of the data in different ways. This methodology is based upon the unique characteristics of sequential covering classification algorithms. It modifies the tuples of sensitive rules of a dataset D in such a way that these are distributed to the "more important" non-sensitive rules. In addition it assures that the tuples belonging to the sensitive rules are assigned to the non-sensitive rules in proportion to their rank in the ruleset. In that way, it is ensured that not only the sensitive rules are hidden but also that the current structure of the ruleset, thus the information value of the dataset, is preserved. Moreover a modification of the basic method which exhibits an alternative distribution procedure is also presented. Finally, a series of experiments are executed in order to evaluate the validity and effectiveness of the proposed approaches against existing similar ones.

A data perturbation approach to sensitive classification rule hiding

Advances in telecommunications and GPS sensors technology have made possible the collection of data like time series of locations, related to the movement of individuals. The analysis of this, so-called trajectory data, is beneficial both for the individuals (e.g., through location-based services) and for the community as a whole (e.g., decision support for urban planning or traffic control). However, because of the very nature of this data, strict safeguards must be enforced to ensure the privacy of the individuals, whose movement is recorded.In this paper, we present a privacy-aware trajectory tracking query engine that offers strict guarantees about what can be observed by untrusted third parties. Through the query engine, subscribed users can gain restricted access to an in-house trajectory data warehouse, to perform certain analysis tasks. In addition to regular queries involving non-spatial non-temporal attributes, the engine supports a variety of spatiotemporal queries, including range queries, nearest neighbor queries and queries for aggregate statistics. The query results are augmented with fake trajectory data (dummies) to fulfil the requirements of K-anonymity. Through qualitative analysis, we prove the effectiveness of our approach towards blocking certain types of attacks, while minimally distorting the dataset.

/pdf/a-privacy-aware-trajectory-tracking-query-engine-5a8ryi7y9h.pdf

A privacy-aware trajectory tracking query engine

In this work, we propose Bit Vectors (BV), an accurate, distance-preserving encoding scheme for representing numerical data values in privacy-preserving tasks. Although many methods have been proposed in the literature for encoding strings, the problem of encoding numerical values has not been effectively addressed yet. In Privacy-Preserving Record Linkage (PPRL), a number of data custodians encode their records and submit them to a trusted third-party that is responsible to identify those records that refer to the same real-world entity. BV is supported by a strong theoretical foundation for embedding numerical values into an anonymization space in a way that preserves the initial distances. Key components of this embedding process are (a) the employed hash functions which, by utilizing random intervals, they allow for approximate matching, and (b) the threshold that is required by the distance computations, which we prove that can be specified in a way that guarantees accurate results.

Distance-Aware Encoding of Numerical Values for Privacy-Preserving Record Linkage

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