Abstract
Data published online nowadays needs a high level of privacy to gain confidentiality as well as to maintain the privacy laws. The focus on k-anonymity enhancements along the last decade, allows this method to be elected as the starting point of any research. In this paper we focus on the external anonymization through a new method: the « Flexible k-anonymity » . It aims to anonymize external published data, by defining a semantic ontology that distinguishes between sparse and abundant quasi-identifiers, and describes aggregation levels relations, in order to achieve adequate k-blocks. For the validation of our proposal, we apply the aforementioned anonymization method to the Comiqual dataset. Comiqual (Collaborative measurement of internet quality), is a large-scale measurement platform for assessing the internet quality access of mobile and ADSL users by collecting mobility traces and private data related to internet metric values.
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References
Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10(05), 557–570 (2002)
Lv, P.: Utility-based anonymization for continuous data publishing. In: Computational Intelligence and Industrial Application. Pacific-Asia Workshop (2008)
Omran, E., Bokma, A., Abu-Almaati, S.: A k-anonymity based semantic model for protecting personal information and privacy. In: 2009 IEEE (IACC 2009), Patiala, India, 6–7 2009
Xiao, Z., Meng, X.: p-sensitivity: a semantic privacy-protection model for location-based services. In: Mobile Data Management Workshops (2008 MDMW) (2008)
Daubert, J., Grube, T., Muhlhauser, M., Fischer, M.: Internal attacks in anonymous publish-subscribe P2P overlays. In: 2015 International Conference on NetSys (2015)
Liu, J., Wang, K.: Enforcing vocabulary k-anonymity by semantic similarity based clustering, in data mining. In: 2010 IEEE 10th International Conference on ICDM (2010)
Junwu, Z., Bin, L., Fei, W., Sicheng, W.: Mobile ontology. Int. J. Digit. Content 4(5), 46–54 (2010)
Ringenberg, T., Taylor, J.: Semantic Anonymization of Medical Records. IEEE, San Diego (2014)
Bertino, E., Ooi, B., Yang, Y., Deng, R.: Privacy and ownership preserving of outsourced medical data. In: 2005 ICDE (2005)
You, T.-H., Peng, W.-C., Lee, W.-C.: Protecting moving trajectories with dummies. In: Proceedings of the 2007 International Conference on Mobile Data, pp. 278–282 (2007)
Kido, H., Yanagisawa, Y., Satoh, T.: An anonymous communication technique using dummies for location-based services. In: ICPS, pp. 88–97 (2005)
Meyeson, A., Williams, R.: On the complexity of optimal K-anonymity. In: PODS 2004, New York (2004)
Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: l-diversity: privacy beyond K-anonymity. In: ICDE (2006)
Li, N., Li, T., Venkatasubramanian, S.: t-closeness: privacy beyond k-anonymity and l-diversity. In: ICDE 2007, pp. 106–115 (2007)
Gambs, S., Killijian, M.-O., Núñez, M., del Cortez, P.: De-anonymization attack on geolocated data. J. Comput. Syst. Sci. 80(8), 1597–1614 (2014). http://dx.doi.org/10.1016/j.jcss.2014.04.024
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Kilany, R., Sokhn, M., Hellani, H., Shabani, S. (2016). Towards Flexible K-Anonymity. In: Ngonga Ngomo, AC., Křemen, P. (eds) Knowledge Engineering and Semantic Web. KESW 2016. Communications in Computer and Information Science, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-319-45880-9_22
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DOI: https://doi.org/10.1007/978-3-319-45880-9_22
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