The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

A privacy preserving association rule mining algorithm was introducedThis algorithm preserved privacy of individual values by computing scalar productMeanwhile the algorithm of computing scalar product was given and the security was analyzed

Privacy preserving association rule mining in vertically partitioned data

Three-Way Multidimensional Scaling

Data mining is gaining societal momentum due to the ever increasing availability of large amounts of human data, easily collected by a variety of sensing technologies. We are therefore faced with unprecedented opportunities and risks: a deeper understanding of human behavior and how our society works is darkened by a greater chance of privacy intrusion and unfair discrimination based on the extracted patterns and profiles. Consider the case when a set of patterns extracted from the personal data of a population of individual persons is released for a subsequent use into a decision making process, such as, e.g., granting or denying credit. First, the set of patterns may reveal sensitive information about individual persons in the training population and, second, decision rules based on such patterns may lead to unfair discrimination, depending on what is represented in the training cases. Although methods independently addressing privacy or discrimination in data mining have been proposed in the literature, in this context we argue that privacy and discrimination risks should be tackled together, and we present a methodology for doing so while publishing frequent pattern mining results. We describe a set of pattern sanitization methods, one for each discrimination measure used in the legal literature, to achieve a fair publishing of frequent patterns in combination with two possible privacy transformations: one based on $$k$$k-anonymity and one based on differential privacy. Our proposed pattern sanitization methods based on $$k$$k-anonymity yield both privacy- and discrimination-protected patterns, while introducing reasonable (controlled) pattern distortion. Moreover, they obtain a better trade-off between protection and data quality than the sanitization methods based on differential privacy. Finally, the effectiveness of our proposals is assessed by extensive experiments.

Discrimination- and privacy-aware patterns

The sharing of sensitive personal data has become a core element of biomedical research. To protect privacy, a broad spectrum of techniques must be implemented, including data anonymization. In this article, we present ARX, an anonymization tool for structured data which supports a broad spectrum of methods for statistical disclosure control by providing (1) models for analyzing re-identification risks, (2) risk-based anonymization, (3) syntactic privacy criteria, such as k-anonymity, l-diversity, t-closeness and δ-presence, (4) methods for automated and manual evaluation of data utility, and (5) an intuitive coding model using generalization, suppression and microaggregation. ARX is highly scalable and allows for anonymizing datasets with several millions of records on commodity hardware. Moreover, it offers a comprehensive graphical user interface with wizards and visualizations that guide users through different aspects of the anonymization process. ARX is not just a toolbox, but a fully-fledged application, meaning that all implemented methods have been harmonized and integrated with each other. It is well understood that balancing privacy and data utility requires user feedback. To facilitate this interaction, ARX is highly configurable and provides various methods for exploring the solution space.

Putting Statistical Disclosure Control into Practice: The ARX Data Anonymization Tool

Gaining access to high-quality data is a vital necessity in knowledge-based decision making. But data in its raw form often contains sensitive information about individuals. Providing solutions to this problem, the methods and tools of privacy-preserving data publishing enable the publication of useful information while protecting data privacy. Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques presents state-of-the-art information sharing and data integration methods that take into account privacy and data mining requirements. The first part of the book discusses the fundamentals of the field. In the second part, the authors present anonymization methods for preserving information utility for specific data mining tasks. The third part examines the privacy issues, privacy models, and anonymization methods for realistic and challenging data publishing scenarios. While the first three parts focus on anonymizing relational data, the last part studies the privacy threats, privacy models, and anonymization methods for complex data, including transaction, trajectory, social network, and textual data. This book not only explores privacy and information utility issues but also efficiency and scalability challenges. In many chapters, the authors highlight efficient and scalable methods and provide an analytical discussion to compare the strengths and weaknesses of different solutions.

Philip Yu

Papers

Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques