Mehmet Ercan Nergiz
ABSTRACT
Trajectory datasets are becoming more and more popular due to the massive usage of GPS and other location-based devices and services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We provide privacy protection by definig trajectory k-anonymity, meaning every released information refers to at least k users/trajectories. We propose a novel generalization-based approach that applies to trajectories and sequences in general. We also suggest the use of a simple random reconstruction of the original dataset from the anonymization, to overcome possible drawbacks of generalization approaches.
We present a utility metric that maximizes the probability of a good representation and propose trajectory anonymization techniques to address time and space sensitive applications. The experimental results over synthetic trajectory datasets show the effectiveness of the proposed approach.
- C. C. Aggarwal and P. S. Yu. A condensation approach to privacy preserving data mining. In EDBT'04, pages 183--199, Heraklion, Crete, Greece, Mar. 14 2004.Google Scholar
Cross Ref
- G. Agrawal, T. Feder, K. Kenthapadi, S. Khuller, R. Panigrahy, D. Thomas, and A. Zhu. Achieving anonymity via clustering. In PODS '06: Proc. of the 25th ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pages 153--162, Chicago, IL, USA, June 26-28 2006. Google ScholarDigital Library
- V. S. V. Aris Gkoulalas-Divanis. A free terrain model for trajectory k-anonymity. In 19th International Conference on Database and Expert Systems Applications - DEXA '08, pages 49--56, 2008. Google ScholarDigital Library
- M. Atzori. Weak -anonymity: A low-distortion model for protecting privacy. In ISC, pages 60--71, 2006. Google ScholarDigital Library
- C. Bettini, X. S. Wang, and S. Jajodia. Protecting privacy against location-based personal identification. In Secure Data Management, pages 185--199, 2005. Google ScholarDigital Library
- F. Bonchi, O. Abul, and M. Nanni. Never walk alone: Uncertainty for anonymity in moving objects databases. In Proceedings of the 24nd International Conference on Data Engineering (ICDE '08), Cancun, Mexico, Apr. 7 2008. Google ScholarDigital Library
- J.-W. Byun, A. Kamra, E. Bertino, and N. Li. Efficient k-anonymization using clustering techniques. In (DASFAAŠ07), Apr. 2007. Google ScholarDigital Library
- J.-W. Byun, Y. Sohn, E. Bertino, and N. Li. Secure anonymization for incremental datasets. In Third VLDB Workshop on Secure Data Management (SDM'06), Seoul, Korea, Sept. 18 2006. Google ScholarDigital Library
- L. Chen and R. Ng. The marriage of lp-norms and edit distance, 2004.Google Scholar
- R. Cheng, Y. Zhang, E. Bertino, and S. Prabhakar. Preserving user location privacy in mobile data management infrastructures. In 6th Workshop Privacy Enhancing Technology Workshop, pages 393--412. Springer, 2006. Google ScholarDigital Library
- M. Diomo and S. Ayman. Potential use of gps data for calibrating travel demand models. In 10th National Conference on Transportation Planning for Small and Medium-Sized Communities, Nashville Tennessee, USA, Sept. 13-15 2006.Google Scholar
- J. Domingo-Ferrer and V. Torra. Ordinal, continuous and heterogeneous k-anonymity through microaggregation. Data Min. Knowl. Discov., 11(2):195--212, 2005. Google ScholarDigital Library
- M. Duckham and L. Kulik. A formal model of obfuscation and negotiation for location privacy. In Pervasive, pages 152--170, 2005. Google ScholarDigital Library
- European Parliament. Directive 95/46/ec. http://www.cdt.org/privacy/eudirective/EU_Directive_.html, 1995.Google Scholar
- European Parliament. Regulation (ec) no 45/2001. http://ec.europa.eu/justice_home/fsj/privacy/docs/application/286_en.pdf, Dec. 18 2000.Google Scholar
- B. Gedik and L. Liu. Location privacy in mobile systems: A personalized anonymization model. In The 25th International Conference on Distributed Computing Systems (ICDCS'05), 2005. Google ScholarDigital Library
- G. Ghinita, P. Kalnis, A. Khoshgozaran, C. Shahabi, and K.-L. Tan. Private queries in location based services: anonymizers are not necessary. In SIGMOD '08: Proceedings of the 2008 ACM SIGMOD international conference on Management of data, pages 121--132, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- M. Gruteser and D. Grunwald. Anonymous usage of location-based services through spatial and temporal cloaking. In Proceedings of the 1st International Conference on Mobile Systems, Applications, and Services, 2003. Google ScholarDigital Library
- M. Gruteser and X. Liu. Protecting privacy in continuous location-tracking applications. IEEE Security and Privacy, 02(2):28--34, 2004. Google ScholarDigital Library
- D. Gusfield. Efficient methods for multiple sequence alignment with guaranteed error bounds. In Bull. Math. Biol., pages 141--154, 1993.Google Scholar
- The health insurance portability and accountability act of 1996. Technical Report Federal Register 65 FR 82462, Department of Health and Human Services, Office of the Secretary, Dec. 2000.Google Scholar
- B. Hoh and M. Gruteser. Protecting location privacy through path confusion. In SECURECOMM '05: Proceedings of the First International Conference on Security and Privacy for Emerging Areas in Communications Networks (SECURECOMM'05), pages 194--205, Washington, DC, USA, 2005. IEEE Computer Society. Google ScholarDigital Library
- B. Hoh, M. Gruteser, H. Xiong, and A. Alrabady. Preserving privacy in gps traces via density-aware path cloaking. In ACM Conference on Computer and Communications Security (CCS), VA, USA, Oct. 29 2007. Google ScholarDigital Library
- A. O. hrn and L. Ohno-Machado. Using boolean reasoning to anonymize databases. Artificial Intelligence in Medicine, 15(3):235--254, Mar. 1999.Google ScholarCross Ref
- T. Jiang and L. Wang. On the complexity of multiple sequence alignment. J. Computer Biologyy, 1:337--348, 1994.Google ScholarCross Ref
- K. LeFevre, D. J. DeWitt, and R. Ramakrishnan. Mondrian multidimensional k-anonymity. In Proceedings of the 22nd International Conference on Data Engineering (ICDE '06), pages 25--35, Atlanta, GA, Apr. 3-7 2006. Google ScholarDigital Library
- N. Li and T. Li. t-closeness: Privacy beyond k-anonymity and l-diversity. In Proceedings of the 23nd International Conference on Data Engineering (ICDE '07), Istanbul, Turkey, Apr. 16-20 2007.Google ScholarCross Ref
- D. Luper, D. Cameron, J. A. Miller, and H. R. Arabnia. Spatial and temporal target association through semantic analysis and gps data mining. In The 2007 World Congress in Computer Science, Computer Engineering, & Applied Computing (IKE'07), Las Vegas, USA, June 25-28 2007.Google Scholar
- A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam. l-diversity: Privacy beyond k-anonymity. In Proc. of the 22nd IEEE Int'l Conf. on Data Engineering (ICDE 2006), Atlanta Georgia, Apr. 2006. Google ScholarDigital Library
- T. McGhee. Gps technology tracks employees. http://www.denverpost.com/headlines/ci_4800440, 2006.Google Scholar
- MIT SENSEable City Lab. Real time rome. http://senseable.mit.edu/realtimerome/, 2006.Google Scholar
- M. F. Mokbel, C.-Y. Chow, and W. G. Aref. The new casper: query processing for location services without compromising privacy. In VLDB '06: Proceedings of the 32nd international conference on Very large data bases, pages 763--774. VLDB Endowment, 2006. Google ScholarDigital Library
- M. E. Nergiz, M. Atzori, and C. Clifton. Hiding the presence of individuals in shared databases. In SIGMOD '07: Proceedings of the 2007 ACM SIGMOD international conference on Management of data, Beijing, China, June 11-14 2007. Google ScholarDigital Library
- M. E. Nergiz, M. Atzori, and C. Clifton. Towards trajectory anonymization: a generalization-based approach. Technical Report TR-08-015, Purdue University, 2008.Google ScholarDigital Library
- M. E. Nergiz and C. Clifton. Thoughts on k-anonymization. Data and Knowledge Engineering, 63(3):622--645, Dec. 2007. Google ScholarDigital Library
- M. E. Nergiz, C. Clifton, and A. E. Nergiz. Multirelational k-anonymity. In Proceedings of the 23nd International Conference on Data Engineering (ICDE '07), Istanbul, Turkey, Apr. 16-20 2007.Google ScholarCross Ref
- Norwich Union. Pay as you drive. http://www.norwichunion.com/pay-as-you-drive/, 2007.Google Scholar
- P. Samarati. Protecting respondents' identities in microdata release. IEEE Transactions on Knowledge and Data Engineering, 13(6):1010--1027, 2001. Google ScholarDigital Library
- L. Sweeney. k-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst., 10(5):557--570, 2002. Google ScholarDigital Library
- M. Terrovitis and N. Mamoulis. Privacy preservation in the publication of trajectories. Mobile Data Management, 2008. MDM '08. 9th International Conference on, pages 65--72, April 2008. Google ScholarDigital Library
- T. M. Truta and A. Campan. k-anonymization incremental maintenance and optimization techniques. In ACM Symposium on Applied Computing (SAC2007), page 380 -- 387, Seoul, Korea, 2007. Google ScholarDigital Library
- US Department of Transportation. Measuring day-to-day variability in travel behavior using gps data. http://www.fhwa.dot.gov/ohim/gps/conclusion.html, 2006.Google Scholar
- R. C.-W. Wong, A. W.-C. Fu, K. Wang, and J. Pei. Minimality attack in privacy preserving data publishing. In VLDB '07: Proceedings of the 33rd international conference on Very large data bases, pages 543--554. VLDB Endowment, 2007. Google ScholarDigital Library
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- Towards trajectory anonymization: a generalization-based approach
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