TY  - BOOK
AU  - Nin,Jordi
AU  - Herranz,Javier
ED  - SpringerLink (Online service)
TI  - Privacy and Anonymity in Information Management Systems: New Techniques for New Practical Problems 
T2  - Advanced Information and Knowledge Processing,
SN  - 9781849962384
AV  - QA76.9.A25 
U1  - 005.8 23
PY  - 2010///
CY  - London
PB  - Springer London, Imprint: Springer
KW  - Computer science
KW  - Data protection
KW  - Computer Science
KW  - Systems and Data Security
N1  - Overview -- to Privacy and Anonymity in Information Management Systems -- Advanced Privacy-Preserving Data Management and Analysis -- Theory of SDC -- Practical Applications in Statistical Disclosure Control Using R -- Disclosure Risk Assessment for Sample Microdata Through Probabilistic Modeling -- Exploiting Auxiliary Information in the Estimation of Per-Record Risk of Disclosure -- Statistical Disclosure Control in Tabular Data -- Preserving Privacy in Distributed Applications -- From Collaborative to Privacy-Preserving Sequential Pattern Mining -- Pseudonymized Data Sharing -- Privacy-Aware Access Control in Social Networks: Issues and Solutions
N2  - 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
UR  - http://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-1-84996-238-4
ER  -