Abstract
In this paper, we discuss the database inference problem. We look at both query-based and partial view-based cases of the problem, concentrating our efforts on classification rules related to the partial view-based case. Based on this analysis, we develop a theoretical formulation to quantify the amount of private information that may be inferred from a public database and we discuss ways to mitigate that inference. Finally, we apply this formulation to actual downgrading issues. Our results are dependent upon the knowledge engine used to derive classification rules. We use C4.5 since it is a well-known and popular robust software tool.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Duncan, G., Lambert, D.: The Risk of Disclosure for Microdata. Jour. of Business & Economic Statistics 7(2), 207–217 (1989)
Duncan, G., Mukherjee, S.: Microdata Disclosure Limitation in Statistical Databases: Query Size and Random Sample Query Control. In: Proc. IEEE Symp. On Security and Privacy, Oakland, CA, pp. 278–287 (1991)
Duncan, G., Pearson, R.: Enhancing Access to Microdata while Protecting Confidentiality: Prospects for the Future. Statistical Science 6(3), 219–239 (1991)
Willenborg, L., de Wall, T.: Statistical Disclosure Control in Practice. In: Händler, W. (ed.) CONPAR 1981. LNCS, vol. 111. Springer, Heidelberg (1981)
Marks, D.: Inference in MLS Database Systems. IEEE Trans. Knowledge and Data Engineering 8(1), 46–55 (1996)
Hinke, T., Delugach, H., Wolf, R.: A Framework for Inference-Directed Data Mining. In: Samarati, Sandhu (eds.) Database Security Vol. X: Status and Prospects. IFIP, pp. 229–239 (1997)
Schafer, J.: Analysis of Incomplete Multivariate Data. Monographs on Statistics and Applied Probability 72. Chapman & Hall, Boca Raton (1997)
Chang, L., Moskowitz, I.S.: Bayesian Methods Applied to the Database Inference Problem. In: Proc. IFIPWG11.3 Working Conf. on Database Security, Greece (1998)
Chang, L., Moskowitz, I.S.: Parsimonious Downgrading and Decision Trees Applied to the Inference Problem. In: Proc. New Security Paradigms 1998, Charlottesville, Virginia (1998)
Moskowitz, I.S., Chang, L.: A Formal View of the Database Inference Problem. In: Mohammadian, M. (ed.) Proc. CIMCA 1999, Vienna, Computational Intelligence for Modelling, Control & Automation, pp. 254–259. IOS Press, Amsterdam (1999)
Moskowitz, I.S., Chang, L.: The Rational Downgrader. In: Proc. PADD 1999, London, UK, April 1999, pp. 159–165 (1999)
Lin, T.Y., Hinke, T., Marks, D., Thuraisingham, B.: Security and Data Mining, Database Security. Status and Prospects, IFIP 9, 391–399 (1996)
Kong Jr., A., Liu, J., Wong, W.: Sequential Imputation and Bayesian Missing Data Problems. Journal of ASA 89(425), 278–288 (1994)
Kang, M., Froscher, J., Moskowitz, I.S.: A Framework for MLS Interoperability. In: Proc. HASE 1996, pp. 198–205 (1996)
Ross Quinlan, J.: C4.5 Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Cachin, C.: An Information–Theoretic Model for Steganography. In: Proc. 2nd International Workshop, Information Hiding 1998, Portland Oregon, April 14-17, pp. 306–318 (1998)
Zöllner, J., Federrath, H., Klimant, H., Pfitzmann, A., Piotraschke, R., Westfeld, A., Wicke, G., Wolf, G.: Modeling the Security of Steganographic Systems. In: Proc. 2nd International Workshop, Information Hiding 1998, Portland Oregon, April 14-17, pp. 344–354 (1998)
Subramonian, R.: Defining diff as a data mining primitive. In: Proc. KDD 1998, pp. 334–338 (1998)
Berger, J.O.: Statistical Decision Theory and Bayesian Analysis, 2nd edn. Springer, Heidelberg (1980)
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proc. ACMSIGMOD Conference, Washington DC (May 1993)
Shannon, C.: Communication Theory of Secrecy Systems, Bell System Technical Journal, 28(4), pp. 656-715 (1949)
Kullback, S., Leibler, R.A.: On Information and Sufficiency. Ann. Math. Stat. 22, 79–86 (1951)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, Chichester (1991)
Foote, J.T., Silverman, H.F.: A Model Distance Measure for Talker Clustering and Identification. In: Proc. ICASSP-1994, pp. 317–320 (1994)
Anderson, R.: Stretching the Limits of Steganography. In: Proc. 1st International Workshop, Information Hiding, Cambridge, UK, May 30-June 1, pp. 39–48 (1996)
Hansen, S., Unger, E.: An Extended Memoryless Inference Control Model: Partial-Table Level Suppression. In: Proc. 1991 Symp. Applied Comp., pp. 142–149 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Moskowitz, I.S., Chang, L. (2000). An Entropy-Based Framework for Database Inference. In: Pfitzmann, A. (eds) Information Hiding. IH 1999. Lecture Notes in Computer Science, vol 1768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10719724_28
Download citation
DOI: https://doi.org/10.1007/10719724_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67182-4
Online ISBN: 978-3-540-46514-0
eBook Packages: Springer Book Archive