Privacy-Preserving Estimation

Mohammad Saad Al-Ahmadi; Rathindra Sarathy

doi:10.4018/978-1-59904-849-9.ch194

Privacy-Preserving Estimation

Mohammad Saad Al-Ahmadi, Rathindra Sarathy

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

2 Scopus citations

Abstract

Data mining has evolved from a need to make sense of the enormous amounts of data generated by organizations. But data mining comes with its own cost, including possible threats to the confidentiality and privacy of individuals. This chapter presents a background on privacy-preserving data mining (PPDM) and the related field of statistical disclosure limitation (SDL). We then focus on privacy-preserving estimation (PPE) and the need for a data-centric approach (DCA) to PPDM. The chapter concludes by presenting some possible future trends.

Original language	English
Title of host publication	Encyclopedia of Artificial Intelligence
Subtitle of host publication	Volume I-III
Publisher	IGI Global
Pages	1323-1329
Number of pages	7
Volume	1-3
ISBN (Electronic)	9781599048505
ISBN (Print)	9781599048499
DOIs	https://doi.org/10.4018/978-1-59904-849-9.ch194
State	Published - 1 Jan 2008

Bibliographical note

Publisher Copyright:
© 2009 by IGI Global. All rights reserved.

ASJC Scopus subject areas

General Computer Science
General Engineering
General Business, Management and Accounting

Access to Document

10.4018/978-1-59904-849-9.ch194

Cite this

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Privacy-Preserving Estimation

Abstract

Bibliographical note

ASJC Scopus subject areas

Access to Document

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