Privacy intrusion detection using dynamic Bayesian networks

ABSTRACT

Concerns for personal information privacy could be produced during information collection, transmission and handling. In information handling, privacy could be compromised from both inside and outside of organizations. Within an organization, private data are generally protected by organizations' privacy policies and the corresponding platforms for privacy practices. However, private data could still be misused intentionally or unintentionally by individuals who have legitimate accesses to them. In general, activities of a database operator form a stochastic process, and at different time, privacy intrusion behavior may show different features. In particular, one's past activities can help determine the natures of his/her current practices. In this paper, we propose to use dynamic Bayesian networks to model such temporal environments and detect any privacy intrusions happened within them.

REFERENCES

• 1: G. Aggarwal, M. Bawa, P. Ganesan, H. Garcia-Molina, K. Kenthapadi, N. Mishra, R. Motwani, U. Srivastava, D. Thomas, J. Widom, and Y. Xu. Enabling privacy for the paranoids. In M. A. Nascimento, M. T. Özsu, D. Kossmann, R. J. Miller, J. A. Blakeley, and K. B. Schiefer, editors, Proceedings of the 13th International Conference on Very Large Data Bases, pages 708--719, Toronto, Canada, August 31-September 3 2004. Morgan Kaufmann.
• 2: X. An, D. Jutla, and N. Cercone. Bayesian network privacy intrusion detection. Technical report, Faculty of Computer Science, Dalhousie University, NS, Canada, 2006.
• 3: A. I. Antón, E. Bertino, N. Li, and T. Yu. A roadmap for comprehensive online privacy policy. Technical report, CERIAS, Purdue University, West Lafayette, CERIAS-2004-47, 2004.
• 4: L. Ardissono, P. Brna, and A. Mitrovic, editors. A comparison of HMMs and dynamic Bayesian networks for recognizing office activities, volume 3538 of Lecture Notes in Computer Science (LNCS), Edinburgh, Scotland, UK, July 24--29 2005. Springer.
• 5: P. Ashley, S. Hada, G. Karjoth, C. Powers, and M. Schunter. Enterprise privacy authorization language (EPAL 1.2). Technical report, W3C Member Submission, http://www.w3.org/Submission/2003/SUBM-EPAL-20031110, November 2003.
• 6: Paul Ashley , Satoshi Hada , Günter Karjoth , Matthias Schunter, E-P3P privacy policies and privacy authorization, Proceedings of the 2002 ACM workshop on Privacy in the Electronic Society, p.103-109, November 21-21, 2002, Washington, DC [doi>10.1145/644527.644538]
• 7: Adam Barth , John C. Mitchell , Justin Rosenstein, Conflict and combination in privacy policy languages, Proceedings of the 2004 ACM workshop on Privacy in the electronic society, October 28-28, 2004, Washington DC, USA [doi>10.1145/1029179.1029195]
• 8: J. G. Cederquist , R. Corin , M. A. C. Dekker , S. Etalle , J. I. den Hartog, An Audit Logic for Accountability, Proceedings of the Sixth IEEE International Workshop on Policies for Distributed Systems and Networks (POLICY'05), p.34-43, June 06-08, 2005 [doi>10.1109/POLICY.2005.5]
• 9: S. Chebrolu, A. Abraham, and J. P. Thomas. Feature deduction and ensemble design of intrusion detection systems. Computers & Security, 24(4):295--307, 2005.
• 10: P. R. ClearingHouse. Privacy today: A review of current issues. Technical report, Privacy Rights Clearinghouse, San Diego, CA, USA. http://www.privacyrights.org, 2002.
• 11: L. Cranor, M. Langheinrich, M. Marchiori, M. Presler-Marshall, and J. Reagle. The platform for privacy preferences 1.0 (P3P 1.0) specification. Technical report, W3C Recommendation, http://www.w3.org/TR/P3P, April 2002.
• 12: Paul Dagum , Adam Galper , Eric Horvitz, Dynamic network models for forecasting, Proceedings of the eighth conference on Uncertainty in Artificial Intelligence, p.41-48, June 1992, Stanford, California, United States
• 13: P. Dagum, A. Galper, E. Horvitz, and A. Seiver. Uncertain reasoning and forescasting. International Journal of Forecasting, 11(1):73--87, 1995.
• 14: T. Dean and K. Kanazawa. Probabilistic temporal reasoning. In Proceedings of the 7th National Conference on Artificial Intelligence (AAAI-1988), pages 524--528, St. Paul, Minnesota, 1988. AAAI Press.
• 15: D. E. Denning and J. Schlörer. Inference control for statistical databases. IEEE Computer, 16(7):69--82, 1983.
• 16: EPIC and PI. Privacy & human rights: An international survey of privacy laws and developments. Technical report, Electronic Privacy Information Center (EPIC), Washington, DC, USA, http://www.epic.org, and Privacy International (PI), London, UK, http://www.privacyinternational.org, 2004.
• 17: EXOCOM. Privacy technology review. Technical report, Business Strategy and IT Consulting Division, The EXOCOM Group Inc. (for the Office of Health and the Information Highway, Health Canada), 2001.
• 18: Z. Ghahramani. An introduction to hidden Markov models and Bayesian networks. International Journal of Pattern Recognition and Artificial Intelligence, 15(1):9--42, 2001.
• 19: Zoubin Ghahramani , Michael I. Jordan, Factorial Hidden Markov Models, Machine Learning, v.29 n.2-3, p.245-273, Nov./Dec. 1997
• 20: V. Gowadia, C. Farkas, and M. Valtorta. Paid: A probabilistic agent-based intrusion detection system. Computers & Security, 24(7):529--545, October 2005.
• 21: J. Alex Halderman , Brent Waters , Edward W. Felten, Privacy management for portable recording devices, Proceedings of the 2004 ACM workshop on Privacy in the electronic society, October 28-28, 2004, Washington DC, USA [doi>10.1145/1029179.1029183]
• 22: S. Hanks, D. Madigan, and J. Gavrin. Probabilistic temporal reasoning with endogenous change. In P. Besnard and S. Hanks, editors, Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence (UAI-1995), Montréal, Québec, Canada, August 18--20 1995. Morgan Kaufmann Publishers.
• 23: Lance J. Hoffman, Computers and Privacy: A Survey, ACM Computing Surveys (CSUR), v.1 n.2, p.85-103, June 1969 [doi>10.1145/356546.356548]
• 24: Sushil Jajodia, Database security and privacy, ACM Computing Surveys (CSUR), v.28 n.1, p.129-131, March 1996 [doi>10.1145/234313.234370]
• 25: K. Johansen and S. Lee. Network security: Bayesian network intrusion detection. Technical report, Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA, 2003.
• 26: P. Kabiri and A. A. Ghorbani. Research on intrusion detection and response: A survey. International Journal of Network Security, 1(2):84--102, 2005.
• 27: Günter Karjoth , Matthias Schunter, A Privacy Policy Model for Enterprises, Proceedings of the 15th IEEE workshop on Computer Security Foundations, p.271, June 24-26, 2002
• 28: Christopher Kruegel , Darren Mutz , William Robertson , Fredrik Valeur, Bayesian Event Classification for Intrusion Detection, Proceedings of the 19th Annual Computer Security Applications Conference, p.14, December 08-12, 2003
• 29: X. Li and Q. Ji. Active affective state detection and user assistance with dynamic Bayesian networks. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 35(1):93--105, 2005.
• 30: Marco Casassa Mont , Siani Pearson , Pete Bramhall, Towards Accountable Management of Identity and Privacy: Sticky Policies and Enforceable Tracing Services, Proceedings of the 14th International Workshop on Database and Expert Systems Applications, p.377, September 01-05, 2003
• 31: A. V. Nefian, L. Liang, X. Pi, and K. Murphy. Dynamic Bayesian networks for audio-visual speech recognition. EURASIP Journal on Applied Signal Processing, 11:1--15, 2002.
• 32: Ann E. Nicholson, Fall Diagnosis using Dynamic Belief Networks, Proceedings of the 4th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence, p.206-217, August 26-30, 1996
• 33: A. E. Nicholson and J. M. Brady. Dynamic belief networks for discrete monitoring. IEEE Transactions on Systems, Man, and Cybernetics, special issue on Knowledge-Based Construction of Probabilistic and Decision Models, 24(11):1593--1610, 1994.
• 34: Hyuk-Jang Park , Sung-Bae Cho, An Effective HMM-Based Intrusion Detection System with Privilege Change Event Modeling, Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence, p.617-618, August 18-22, 2002
• 35: R. G. Parker. Privacy issues: Business impacts and responsibilities. Technical report, CAAA/SAP AG Technology and Accounting Education Seminar Series, Canadian Academic Accounting Association (CAAA), 2005.
• 36: Judea Pearl, Probabilistic reasoning in intelligent systems: networks of plausible inference, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1988
• 37: Xiaolei Qian , Mark E. Stickel , Peter D. Karp , Teresa F. Lunt , Thomas D. Carvey, Detection and Elimination of Inference Channels in Multilevel Relational Database Systems, Proceedings of the 1993 IEEE Symposium on Security and Privacy, p.196, May 24-26, 1993
• 38: L. R. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of IEEE, 77(2):257--286, February 1989.
• 39: A. B. Salem, L. Bouillaut, P. Aknin, and P. Weber. Dynamic Bayesian networks for classification of rail defects. In Proceedings of the Fourth International Conference on Intelligent Systems Design and Applications (ISDA'04), Budapest, Hungary, August 26--28 2004.
• 40: M. Schunter and P. Ashley. The platform for enterprise privacy practices. In Proceedings of the 2002 Information Security Solutions Europe Conference (ISSE'02), Paris, France, October 2--4 2002.
• 41: M. Shroff. Annual report of privacy commissioner 2003-2004. Technical report, Office of the Privacy Commissioner, New Zealand, 2004.
• 42: Padhraic Smyth , David Heckerman , Michael I. Jordan, Probabilistic independence networks for hidden Markov probability models, Neural Computation, v.9 n.2, p.227-269, Feb. 15, 1997 [doi>10.1162/neco.1997.9.2.227]
• 43: Jessica Staddon, Dynamic inference control, Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, June 13-13, 2003, San Diego, California [doi>10.1145/882082.882103]
• 44: R. Sterritt, A. Marshall, C. Shapcott, and S. McClean. Exploring dynamic bayesian belief networks for intelligent fault management systems. In Proceedings of IEEE International Conference on Systems, Man and Cybernetics, pages 3646--3652, September 2000.
• 45: H. S. Venter, M. S. Olivier, and J. H. P. Eloff. PIDS: A privacy intrusion detection system. In S. M. Furnell and P. S. Dowland, editors, Proceedings of the 4th International Network Conference, pages 255--262, Plymouth, UK, July 2004.
• 46: S. Warren and L. Brandeis. The right to privacy. Harvard Law Review, 4:193--220, 1890.
• 47: C. Warrender, S. Forrest, and B. Pearlmutter. Detecting intrusions using system calls: Alternative data models. In Proceedings of 1999 IEEE Symposium on Security & Privacy, pages 133--145, Berkeley, CA, May 9--12 1999.
• 48: W.-K. Wong, G. Cooper, and M. Wagner. Bayesian network anomaly pattern detection for disease outbreaks. In Proceedings of the 20th International Conference on Machine Learning (ICML-2003), Washington DC, USA, 2003.
• 49: Charles Wright , Fabian Monrose , Gerald M. Masson, HMM profiles for network traffic classification, Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security, October 29-29, 2004, Washington DC, USA [doi>10.1145/1029208.1029211]
• 50: R. Yi , K. Levitt, Data Level Inference Detection in Database Systems, Proceedings of the 11th IEEE workshop on Computer Security Foundations, p.179, June 09-11, 1998
• 51: Geoffrey Zweig , Stuart Russell, Speech recognition with dynamic Bayesian networks, Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence, p.173-180, July 1998, Madison, Wisconsin, United States

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.7 Database Administration

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.3 Deduction and Theorem Proving
  I.5 PATTERN RECOGNITION
      I.5.4 Applications

General Terms:
Design, Management, Security

Keywords:
dynamic bayesian networks, intrusion detection, privacy intrusion, probabilistic reasoning