research-article

A Bayesian approach for on-line max auditing of dynamic statistical databases

Authors:

Gerardo Canfora,

Bice CavalloAuthors Info & Claims

EDBT/ICDT '09: Proceedings of the 2009 EDBT/ICDT Workshops

Pages 107 - 116

https://doi.org/10.1145/1698790.1698809

Published: 22 March 2009 Publication History

Abstract

In this paper we propose a method for on-line max auditing of dynamic statistical databases. The method extends the Bayesian approach presented in [2], [3] and [4] for static databases. A Bayesian network addresses disclosures based on probabilistic inferences that can be drawn from released data; we have developed algorithms to update the network whenever the database changes. In particular, we consider the case in which records are added or deleted, or some sensitive values change their value. The paper introduces the algorithms and discusses results of a preliminary set of of experimental trials.

References

[1]

N. R. Adam and J. C. Wortmann. Security-control methods for statistical databases: A comparative study. ACM Comput. Surv., 21(4):515--556, 1989.

Digital Library

[2]

G. Canfora and B. Cavallo. A bayesian approach for on-line max and min auditing. In Proocedings of International workshop on Privacy and Anonymity in Information Society (PAIS), pages 12--20. ACM DL, 2008.

Digital Library

[3]

G. Canfora and B. Cavallo. A bayesian approach for on-line max auditing. In Proocedings of The Third International Conference on Availability, Reliability and Security (ARES), pages 1020--1027. IEEE Computer Society Press, 2008.

Digital Library

[4]

G. Canfora and B. Cavallo. Reasoning under uncertainty in on-line auditing. In Privacy in Statistical Databases, Lecture Notes in Computer Science, volume 5262, pages 257--269. Springer-Verlag Berlin Heidelberg, 2008.

Digital Library

[5]

B. Cavallo. Data Privacy in Statistical Databases. A Bayesian approach to deal with user uncertain knowledge in on-line auditing. PhD thesis, University of Sannio, Software Engineering, June 2008. http://plone.rcost.unisannio.it/canfora/downloads/Bicedissertation.pdf/view.

[6]

F. Y. Chin. Security problems on inference control for sum, max, and min queries. Journal of the ACM, 33(3):451--464, July 1986.

Digital Library

[7]

D. Heckerman. Causal independence for knowledge acquisition and inference. In Proceedings of Ninth Conference on Uncertainty in Artificial Intelligence, pages 122--127, 1993.

Digital Library

[8]

K. Kenthapadi, N. Mishra, and K. Nissim. Simulatable auditing. In PODS, pages 118--127, June 2005.

Digital Library

[9]

J. Kleinberg, C. Papadimitriou, and P. Raghavan. Auditing boolean attributes. Journal of Computer and System Sciences, 66(1):244--253, February 2003.

Digital Library

[10]

F. M. Malvestuto, M. Mezzini, and M. Moscarini. Auditing sum-queries to make a statistical database secure. ACM Transactions on Information and System Security (TISSEC), 9(1):31--60, February 2006.

Digital Library

[11]

M. McLelsh. Further results on the security of partitioned dynamic statistical databases. ACM Transactions on Database Systems, 14(1):98--113, March 1989.

Digital Library

[12]

S. U. Nabar, B. Marthi, K. Kenthapadi, N. Mishra, and R. Motwani. Towards robustness in query auditing. In International Conference on Very Large Data Bases, pages 151--162, 2006.

Digital Library

[13]

K. G. Olesen, U. Kjaerulff, F. Jensen, F. V. Jensen, B. Falck, S. Andreassen, and S. K. Andersen. A munin network for the median nerve - a case study in loops. Applied Artificial Intelligence, 3(2--3):385--403, 1989.

Digital Library

[14]

J. Pearl. Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Francisco, CA, USA, 1998.

Digital Library

[15]

S.-P. Shieh and C.-T. Lin. Auditing user queries in dynamic statistical databases. Information Sciences, 113(1--2):131--146, January 1999.

Digital Library

[16]

S. Srinivas. A generalization of the noise-or-model. In Ninth Annual Conference of Uncertainty on AI, pages 208--218, 1993.

[17]

M. Watnik. Pay for play: Are baseball salaries based on performance. Journal of Statistics Education, 6, 1998.

Cited By

Cavallo BCanfora GD’Apuzzo LSquillante M(2013)Reasoning under uncertainty and multi-criteria decision making in data privacyQuality & Quantity10.1007/s11135-013-9859-848:4(1957-1972)Online publication date: 12-May-2013
https://doi.org/10.1007/s11135-013-9859-8
Dominic A(2011)Statistical analysis for comparison of the key representation database with the original databaseInternational Journal of Business Information Systems10.1504/IJBIS.2011.0423958:4(339-360)Online publication date: 1-Sep-2011
https://dl.acm.org/doi/10.1504/IJBIS.2011.042395

Index Terms

A Bayesian approach for on-line max auditing of dynamic statistical databases
1. Applied computing
  1. Physical sciences and engineering
    1. Mathematics and statistics
2. Information systems
  1. Data management systems
    1. Database management system engines
  2. Information systems applications
    1. Decision support systems
      1. Data analytics

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cover image ACM Other conferences

EDBT/ICDT '09: Proceedings of the 2009 EDBT/ICDT Workshops

March 2009

218 pages

ISBN:9781605586502

DOI:10.1145/1698790

Copyright © 2009 ACM.

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Association for Computing Machinery

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Publication History

Published: 22 March 2009

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EDBT/ICDT '09

EDBT/ICDT '09: EDBT/ICDT '09 joint conference

March 22, 2009

Saint-Petersburg, Russia

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Overall Acceptance Rate 7 of 10 submissions, 70%

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Cited By

Cavallo BCanfora GD’Apuzzo LSquillante M(2013)Reasoning under uncertainty and multi-criteria decision making in data privacyQuality & Quantity10.1007/s11135-013-9859-848:4(1957-1972)Online publication date: 12-May-2013
https://doi.org/10.1007/s11135-013-9859-8
Dominic A(2011)Statistical analysis for comparison of the key representation database with the original databaseInternational Journal of Business Information Systems10.1504/IJBIS.2011.0423958:4(339-360)Online publication date: 1-Sep-2011
https://dl.acm.org/doi/10.1504/IJBIS.2011.042395

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