Abstract
In recent years, personal data has been shared between organizations and researchers. While sharing information, individuals’ sensitive data should be preserved. For this purpose, a number of algorithms for privacy-preserving publish data have been designed. These algorithms modify or transform data to protect privacy. While the anonymization algorithms such as k-anonymity, l-diversity and t-closeness focus on changing data to a protected form, the differential privacy model considers the results of queries posed on data. Therefore, these algorithms can be compared according to their performance or utility of the queries that have been applied on anonymized data or computed results with noise. In this work, we present a domain-independent semantic model of data anonymization techniques which also considers individuals’ different privacy concerns. Thus, the proposed conceptualized model integrates the generic view of privacy preserving data anonymization algorithms with a personalized privacy approach.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Boillat P., Kjaerum M.: Handbook on European data protection law (2014). http://www.echr.coe.int/Documents/Handbook_data_protection_ENG.pdf
Ciriani V., De Capitani di Vimercati S., Foresti S., Samarati P.: k-Anonymity. In: Advances in Information Security (2007)
Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: L-Diversity: Privacy Beyond k-Anonymity. ACM Transactions on Knowledge Discovery from Data 1(1), Article 3 (2007)
Li N., Li T., Venkatasubramanian S: t-closeness: privacy beyond k-anonymity and l-diversity. In: ICDE Conference 2007 (2007)
Dwork, C.: Differential privacy: a survey of results. In: Agrawal, M., Du, D.-Z., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008)
Sarathy, R., Muralidhar, K.: Evaluating Laplace Noise Addition to Satisfy Differential Privacy for Numeric Data. Transactions on Data Privacy 4 (2011)
Aggarwal, C., Yu, P.: Privacy-Preserving Data Mining : Models and Algorithms. Springer Publishing (2008). ISBN:0387709916 9780387709918
Agrawal R., Srikant R.: Privacy preserving data mining. In: Proceedings of the ACM SIGMOID Conference (2000)
Sweeney, L.: k-Anonymity: A model for protecting privacy. Int. Journal on Uncertainty, Fuzziness and Knowledge-Based Systems 10(5), 557–570 (2002)
Wong, R., Li, J., Fu, A., Wang, K.: (\({\alpha }\), k)-anonymity: an enhanced k-anonymity model for privacy-preserving data publishing. In: Proceedings of the 12th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 754–759 (2006)
Fraser R., Willison D.: Tools for De-Identification of Personal Health Information. Pan Canadian Health Information (HIP) Group (2009)
Jin, H.W.: Practical issues on privacy-preserving health data mining. In: Washio, T., Zhou, Z.-H., Huang, J.Z., Hu, X., Li, J., Xie, C., He, J., Zou, D., Li, K.-C., Freire, M.M. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4819, pp. 64–75. Springer, Heidelberg (2007)
Divanis, A., et al.: Publishing data from electronic health records while preserving privacy: A survey of algorithms. J. of Biomedical Informatics 50, 4–19 (2014)
Qin, J., Li, X.: A framework for privacy preserving medical document sharing. In: Thirty Fourth Int. Conf. on Information Systems (2013)
Xiao, X., Tao, Y.: Personalized privacy preservation. In: ACM SIGMOD Int. Conf. on Management of Data, pp. 229–240 (2006)
Omran, E., Bokma, A., Abu-Almaati, S.: A k-anonymity based semantic model for protecting personal information and privacy. In: IEEE Int. Advance Computing Conference (2009)
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
Usenmez, B., Can, O. (2015). Conceptualization of Personalized Privacy Preserving Algorithms. In: Garoufallou, E., Hartley, R., Gaitanou, P. (eds) Metadata and Semantics Research. MTSR 2015. Communications in Computer and Information Science, vol 544. Springer, Cham. https://doi.org/10.1007/978-3-319-24129-6_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-24129-6_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24128-9
Online ISBN: 978-3-319-24129-6
eBook Packages: Computer ScienceComputer Science (R0)