Abstract
K-Anonymity algorithms are used as essential methods to protect end users’ data privacy. However, state-of-art K-Anonymity algorithms have shortcomings such as lacking generalization and suppression value priority standard. Moreover, the complexity of these algorithms are usually high. Thus, a more robust and efficient K-Anonymity algorithm is needed for practical usage. In this paper, a novel K-Anonymous full domain generalization algorithm based on heap sort is presented. We first establish the k-anonymous generalization priority standard of information. Then our simulation results show the user’s data privacy can be effectively protected while generalization efficiency is also improved.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adusumalli, S.K., Kumari, V.V.: Attribute based anonymity for preserving privacy. In: Abraham, A., Mauri, J.L., Buford, J.F., Suzuki, J., Thampi, S.M. (eds.) ACC 2011. CCIS, vol. 193, pp. 572–579. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22726-4_59
Berčič, B., George, C.: Identifying personal data using relational database design principles. Int. J. Law Inf. Technol. 17(3), 233–251 (2008)
Gai, K., Qiu, M.: Blend arithmetic operations on tensor-based fully homomorphic encryption over real numbers. IEEE Trans. Ind. Inform. 14(8), 3590–3598 (2018)
Gionis, A., Tassa, T.: k-anonymization with minimal loss of information. IEEE Trans. Knowl. Data Eng. 21(2), 206–219 (2009)
Li, Y., Dai, W., Ming, Z., Qiu, M.: Privacy protection for preventing data over-collection in smart city. IEEE Trans. Comput. 65(5), 1339–1350 (2016)
Qiu, M., Gai, K., Thuraisingham, B., Tao, L., Zhao, H.: Proactive user-centric secure data scheme using attribute-based semantic access controls for mobile clouds in financial industry. Futur. Gener. Comput. Syst. 80, 421–429 (2018)
Sai Kumar, K.: Achieving k-anonymity using parallelism in full domain generalization. Ph.D. thesis (2015)
Samarati, P., Sweeney, L.: Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. Technical report, SRI International (1998)
Shi, P., Xiong, L., Fung, B.: Anonymizing data with quasi-sensitive attribute values. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1389–1392. ACM (2010)
Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(05), 571–588 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhou, X., Qiu, M. (2018). A K-Anonymous Full Domain Generalization Algorithm Based on Heap Sort. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2018. Lecture Notes in Computer Science(), vol 11344. Springer, Cham. https://doi.org/10.1007/978-3-030-05755-8_44
Download citation
DOI: https://doi.org/10.1007/978-3-030-05755-8_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05754-1
Online ISBN: 978-3-030-05755-8
eBook Packages: Computer ScienceComputer Science (R0)