Abstract
Mining crime reports in real-time is useful in improving the response time of law enforcement authorities in addressing crime. However, limitations on computational processing power and in-house mining expertise make this challenging, particularly so for law enforcement agencies in technology constrained environments. Outsourcing crime data mining offers a cost-effective alternative strategy. Yet outsourcing crime data raises the issue of user privacy. Therefore encouraging user participation in crime reporting schemes is conditional on providing strong guarantees of personal data protection. Cryptographic approaches make for time consuming query result generation, so the preferred approach is to anonymize the data. Mining real-time crime data as opposed to static data facilitates fast intervention. To achieve this goal, Sakpere and Kayem presented a preliminary solution based on the notion of buffering. Buffering improves on information loss significantly in comparison with previous solutions. In this paper, we extend the Sakpere and Kayem result to support user privacy expressions. We achieve this by integrating a three-tiered user-defined privacy preference model in data stream process. The three-tiered model offers a simple and generic approach to classifying the data without impacting negatively on information loss. Results from our proof-of-concept implementation indicate that incorporating user privacy preferences reduces the rate of information loss due to misclassification.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
These are environments that are characterized by low computational and processing resources. Examples emerge in disaster scenarios and remote areas.
- 2.
- 3.
- 4.
Further details about the app can be found in http://cryhelp.cs.uct.ac.za/download.
References
Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10(05), 557–570 (2002)
Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10(05), 571–588 (2002)
Iyengar, V.S.: Transforming data to satisfy privacy constraints. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 279–288. ACM (2002)
Kabir, M.E., Wang, H., Bertino, E.: Efficient systematic clustering method for k-anonymization. Acta Informatica 48(1), 51–66 (2011)
Cao, J., Carminati, B., Ferrari, E., Tan, K.L.: Castle: continuously anonymizing data streams. IEEE Trans. Dependable Secure Comput. 8(3), 337–352 (2011)
Guo, K., Zhang, Q.: Fast clustering-based anonymization approaches with time constraints for data streams. Knowledge-Based Systems, Elsevier (2013, in press)
Sakpere, A.B.: User-defined privacy preferences for k-anonymization in electronic crime reporting systems for developing nations. In: Doctoral Consortium, pp. 13–18 (2015). doi:10.5220/0005364700130018
Sakpere, A.B., Anne, V.D.M.K., Marchetti-Mercer, M.C.: Adaptive buffer resizing for efficient anonymization of streaming data with minimal information loss. In: Proceedings of the 1st International Conference on Information Systems Security and Privacy, pp. 191–201 (2015). doi:10.5220/0005288901910201
Stone, C.: Crime, justice, and growth in South Africa: toward a plausible contribution from criminal justice to economic growth. John F. Kennedy School of Government Working Paper No. RWP06-038(2006)
Li, S.: Fuzzy optimization and decision making. Poisson Process with Fuzzy Rates, pp. 289–305. Kluwer Academic Publishers, Hingham (2010)
Aggarwal, C.C., Yu, P.S. (eds.): TA General Survey of Privacy-preserving Data Mining Models and Algorithms. Springer, Heidelberg (2008)
Xiao, X., Tao, Y.: Personalized privacy preservation. In: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data. ACM (2006)
Gedik, B., Liu, L.: Protecting location privacy with personalized k-anonymity: architecture and algorithms. IEEE Trans. Mob. Comput. 7(1), 1–18 (2008)
Zakerzadeh, H., Osborn, S.L.: FAANST: Fast Anonymizing Algorithm for Numerical Streaming DaTa. In: Garcia-Alfaro, J., Navarro-Arribas, G., Cavalli, A., Leneutre, J. (eds.) DPM 2010 and SETOP 2010. LNCS, vol. 6514, pp. 36–50. Springer, Heidelberg (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Sakpere, A.B., Kayem, A.V.D.M. (2015). Supporting Streaming Data Anonymization with Expressions of User Privacy Preferences. In: Camp, O., Weippl, E., Bidan, C., Aïmeur, E. (eds) Information Systems Security and Privacy. ICISSP 2015. Communications in Computer and Information Science, vol 576. Springer, Cham. https://doi.org/10.1007/978-3-319-27668-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-27668-7_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27667-0
Online ISBN: 978-3-319-27668-7
eBook Packages: Computer ScienceComputer Science (R0)