| Privacy in Statistical Databases: UNESCO Chair in Data Privacy International Conference, Psd 2008, Istanbul, Turkey, September 24-26, 2008, Proceeding 2008 Edition Contributor(s): Domingo-Ferrer, Josep (Editor), Saygin, Yücel (Editor) |
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ISBN: 3540874704 ISBN-13: 9783540874706 Publisher: Springer OUR PRICE: $85.49 Product Type: Paperback - Other Formats Published: September 2008 Annotation: This book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2008, held in September 2008 in Istanbul, Turkey, under the sponsorship of the UNESCO chair in Data Privacy. The 27 revised full papers presented were carefully reviewed and selected from 37 submissions. The papers are organized in topical sections on tabular data protection; microdata protection; online databases and remote access; privacy-preserving data mining and private information retrieval; and legal issues. |
| Additional Information |
| BISAC Categories: - Computers | Databases - Data Mining - Mathematics | Probability & Statistics - General - Computers | Intelligence (ai) & Semantics |
| Dewey: 005.8 |
| Series: Lecture Notes in Computer Science |
| Physical Information: 0.8" H x 6.1" W x 9.3" (1.15 lbs) 335 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: Privacy in statistical databases is a discipline whose purpose is to provide solutions to the tension between the increasing social, political and economical demand of accurate information, and the legal and ethical obligation to protect the privacy of the various parties involved. Those parties are the respondents (the individuals and enterprises to which the database records refer), the data owners (those organizations spending money in data collection) and the users (the ones querying the database, who would like their queries to stay con?d- tial). Beyond law and ethics, there are also practical reasons for data collecting agencies to invest in respondent privacy: if individual respondents feel their p- vacyguaranteed, they arelikelyto providemoreaccurateresponses. Data owner privacy is primarily motivated by practical considerations: if an enterprise c- lects data at its own expense, it may wish to minimize leakage of those data to other enterprises (even to those with whom joint data exploitation is planned). Finally, user privacy results in increased user satisfaction, even if it may curtail the ability of the database owner to pro?le users. Thereareatleasttwotraditionsinstatisticaldatabaseprivacy, bothofwhich started in the 1970s: one stems from o?cial statistics, where the discipline is also known as statistical disclosure control (SDC), and the other originatesfrom computer science and database technology. In o?cial statistics, the basic c- cern is respondent privac |