This book addresses the issue of "hiding" sensitive association rules, and introduces a number of heuristic answers. It presents recently discovered solutions of increased time complexity, as well as a number of computationally efficient parallel approaches.

Privacy and security risks arising from the application of different data mining techniques to large institutional data repositories have been solely investigated by a new research domain, the so-called privacy preserving data mining. Association rule hiding is a new technique in data mining, which studies the problem of hiding sensitive association rules from within the data.

Association Rule Hiding for Data Mining addresses the problem of "hiding" sensitive association rules, and introduces a number of heuristic solutions. Exact solutions of increased time complexity that have been proposed recently are presented, as well as a number of computationally efficient (parallel) approaches that alleviate time complexity problems, along with a thorough discussion regarding closely related problems (inverse frequent item set mining, data reconstruction approaches, etc.). Unsolved problems, future directions and specific examples are provided throughout this book to help the reader study, assimilate and appreciate the important aspects of this challenging problem.

Association Rule Hiding for Data Mining is designed for researchers, professors and advanced-level students in computer science studying privacy preserving data mining, association rule mining, and data mining. This book is also suitable for practitioners working in this industry.

This book is among the pioneer efforts regarding the development of Association Rule Hiding Provides examples throughout this book to help the reader study, assimilate and appreciate the important aspects of this challenging problem Covers closely related problems (inverse frequent itemset mining, data reconstruction approaches, etc.), unsolved problems and future directions Includes supplementary material: sn.pub/extras

Klappentext

Privacy and security risks arising from the application of different data mining techniques to large institutional data repositories have been solely investigated by a new research domain, the so-called privacy preserving data mining. Association rule hiding is a new technique in data mining, which studies the problem of hiding sensitive association rules from within the data. Association Rule Hiding for Data Mining addresses the problem of "hiding" sensitive association rules, and introduces a number of heuristic solutions. Exact solutions of increased time complexity that have been proposed recently are presented, as well as a number of computationally efficient (parallel) approaches that alleviate time complexity problems, along with a thorough discussion regarding closely related problems (inverse frequent item set mining, data reconstruction approaches, etc.). Unsolved problems, future directions and specific examples are provided throughout this book to help the reader study, assimilate and appreciate the important aspects of this challenging problem. Association Rule Hiding for Data Mining is designed for researchers, professors and advanced-level students in computer science studying privacy preserving data mining, association rule mining, and data mining. This book is also suitable for practitioners working in this industry.

Inhalt
Fundamental Concepts.- Background.- Classes of Association Rule Hiding Methodologies.- Other Knowledge Hiding Methodologies.- Summary.- Heuristic Approaches.- Distortion Schemes.- Blocking Schemes.- Summary.- Border Based Approaches.- Border Revision for Knowledge Hiding.- BBA Algorithm.- Max-Min Algorithms.- Summary.- Exact Hiding Approaches.- Menon's Algorithm.- Inline Algorithm.- Two-Phase Iterative Algorithm.- Hybrid Algorithm.- Parallelization Framework for Exact Hiding.- Quantifying the Privacy of Exact Hiding Algorithms.- Summary.- Epilogue.- Conclusions.- Roadmap to Future Work.