Security of Statistical Databases with an Output Perturbation Technique

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 recoding 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.Key Words and Phrases: Database managementstatistical databasesdatabase security Additional informationNotes on contributorsNabil R. AdamNabil R. Adam is Associate Professor at the Graduate School of Management of Rutgers University. He received his M.S., M.Phil., and Ph.D. from Columbia University. He has contributed to ACM Computing Surveys, European Journal of Operational Research, Management Science, and IEEE Transactions. He has edited a special issue on Database Management for JMIS, on Computer Simulation for Communications of the ACM, and has co-edited a special issue on Simulation for Operations Research. His research interests include database security, concurrency control in distributed database systems, computer simulation and scheduling. He has served as a consultant in the area of Database Management to several major organizations.Douglas H. JonesDouglas H. Jones is Associate Professor of Quantitative Studies at the Graduate School of Management of Rutgers University. He received his M.S. and Ph.D. from Florida State University, and his B.S. from Florida Atlantic University-Boca Raton. His specialties include Mathematical and Applied Statistics, Mental Measurement, and Data Analysis. He has published in the Annals of Statistics, Journal of the American Statistical Association, and Psychometrika.

• 저널Journal of Management Information Systems · 6(1) · 101–110
• 토픽Statistical Methods and Inference · Statistics and Probability
• DOI10.1080/07421222.1989.11517851
• 저자Nabil R. Adam, Douglas H. Jones