Security of Statistical Databases with an Output Perturbation Technique.

Saved in:

Bibliographic Details
Title:	Security of Statistical Databases with an Output Perturbation Technique.
Authors:	Adam, Nabil R.¹, Jones, Douglas H.²
Source:	Journal of Management Information Systems. Summer89, Vol. 6 Issue 1, p101-110. 10p.
Subjects:	Computers in statistics, Database security, Perturbation theory, Database management, Computer security, Regression analysis, Statistical services
Abstract:	A statistical database aims at providing users with statistics about the population while not compromising the confidentiality of the individuals whose data are included in the database. Threats to the database security range from issuing cleverly designed sequences of queries to using such sophisticated methods as regression analysis. In order to overcome this security problem, several solution methods have been suggested in the literature. These methods can be classified under four general approaches: conceptual modeling; query restriction; data perturbation; and output perturbation. These methods, however, can be easily compromised, or require excessive CPU and memory, or result in biased response to users. The purpose of this paper is to propose a new type of output perturbation method that may be very difficult to compromise and provides unbiased response. The method is based on recoiling of the data, the jackknifing concept, and an extension of the random sample queries method suggested by Denning [5]. A comparison of the proposed method and the modified random sample queries method (which is considered a viable alternative for security of statistical databases) is presented. [ABSTRACT FROM AUTHOR]
	Copyright of Journal of Management Information Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Engineering Source

Description
Abstract:	A statistical database aims at providing users with statistics about the population while not compromising the confidentiality of the individuals whose data are included in the database. Threats to the database security range from issuing cleverly designed sequences of queries to using such sophisticated methods as regression analysis. In order to overcome this security problem, several solution methods have been suggested in the literature. These methods can be classified under four general approaches: conceptual modeling; query restriction; data perturbation; and output perturbation. These methods, however, can be easily compromised, or require excessive CPU and memory, or result in biased response to users. The purpose of this paper is to propose a new type of output perturbation method that may be very difficult to compromise and provides unbiased response. The method is based on recoiling of the data, the jackknifing concept, and an extension of the random sample queries method suggested by Denning [5]. A comparison of the proposed method and the modified random sample queries method (which is considered a viable alternative for security of statistical databases) is presented. [ABSTRACT FROM AUTHOR]
ISSN:	07421222
DOI:	10.1080/07421222.1989.11517851