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Privacy and Anonymity in Information Management Systems: New Techniques for New Practical Problems - Softcover
Synopsis
List of Contributors .- Part I Overview.- 1. Introduction to Privacy and Anonymity in Information Management Systems.- 2. Advanced Privacy-Preserving Data Management and Analysis.- Part II Theory of SDC.- 3. Practical Applications in Statistical Disclosure Control Using R.- 4. Disclosure Risk Assessment for Sample Microdata Through.- Probabilistic Modeling .- 5. Exploiting Auxiliary Information in the Estimation of Per-record.- Risk of Disclosure.- 6. Statistical Disclosure Control in Tabular Data.- Part III Preserving Privacy in Distributed Applications.- 7. From Collaborative to Privacy Preserving Sequential Pattern.- 8. Pseudonymized Data Sharing.- 9. Privacy-aware Access Control in Social Networks: Issues and Solutions.- Index.
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From the Back Cover
The development of information technologies in the last few years has been remarkable. Large amounts of data are collected and stored by both public institutions and private companies every day. There are clear threats to the privacy of citizens if no care is taken when collecting, storing and disseminating data. Ensuring privacy for individuals in a society when dealing with digital information, is a task which involves many agents, including politicians, legal authorities, managers, developers, and system administrators. Privacy and Anonymity in Information Management Systems deals with the more technical parts of this `privacy cycle', those issues that are mostly related to computer science, and discusses the process by which different privacy mechanisms are motivated, designed, analyzed, tested and finally implemented in companies or institutions. The book is written in such a way that several of the chapters are self-contained and accessible to students, covering topics such as the problem of Statistical Disclosure Control (SDC), i.e. how to modify datasets that contain statistical information before publicly releasing them, and doing so in such a way that the privacy of the confidential original information is preserved; and specific distributed applications involving privacy – how different agents have private inputs but want to cooperate to run some protocol in their own interest, without revealing unnecessary parts of their private inputs. Graduate students and researchers will find this book an excellent resource.
About the Author
Jordi Nin (Barcelona, Catalonia, 1979; BSc 2004, MSc 2007, PhD 2008 all in Computer Science) is a post-doctoral researcher at the Artificial Intelligence Research Institute (IIIA-CSIC) near Barcelona, Catalonia, Spain. His fields of interest are privacy technologies, machine learning and soft computing tools. He has been involved in several research projects funded by the Catalan and Spanish governments and the European Community. His research has been published in specialized journals and major conferences (around 30 papers). Javier Herranz obtained his PhD in Applied Mathematics in 2005, in the Technial University of Catalonia (UPC, Barcelona, Spain). After that he spent 9 months in the Ecole Polytechnique (France) and 9 months in the Centrum voor Wiskunde en Informatica (CWI, The Netherlands), as a post-doctoral researcher, granted with an ERCIM fellowship. From January 2007, he works as a post-doctoral researcher at IIIA-CSIC (Bellaterra, Spain). His research interests include the design and analysis of cryptographic protols and the study of privacy preserving operations involving databases.
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