Abstract
With the significant increase of information sharing in various areas, it has been an important issue to prevent personal information from being disclosed to abnormal users. Pattern mining is one of data mining technique for extracting interesting pattern information from massive databases. Therefore, sensitive patterns belonging to personal information can be disclosed to abnormal users through pattern mining methods. A sanitization approach that modifies a given database is one of the most common approach for achieving privacy preserving. In this paper, we introduce and analyze various methods for achieving privacy preserving in high utility pattern mining based on sanitization approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)
Lin, J.C.-W., Gan, W., Fournier-Viger, P., Yang, L., Liu, Q., Frnda, J., Sevcik, L., Voznak, M.: High utility-itemset mining and privacy preserving utility mining. Perspect. Sci. 7, 74–80 (2016)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Disc. 8(1), 53–87 (2004)
Kim, D., Yun, U.: Efficient mining of high utility pattern with considering of rarity and length. Appl. Intell. 45(1), 152–173 (2016)
Ryang, H., Yun, U.: High utility pattern mining over data streams with sliding window technique. Expert Syst. Appl. 57(15), 214–231 (2016)
Verykios, V.S., Elmagarmid, A.K., Bertino, E., Saygin, Y., Dasseni, E.: Association rule hiding. IEEE Trans. Knowl. Data Eng. 16(4), 434–447 (2004)
Wu, Y.-H., Chiang, C.-M., Chen, L.P.: Hiding sensitive association rules with limited side effects. IEEE Trans. Knowl. Data Eng. 19(1), 29–42 (2007)
Yeh, J.-S., Hsu, P.-C.: HHUIF and MSICF: novel algorithms for privacy preserving utility mining. Expert Syst. Appl. 37, 4779–4786 (2010)
Yun, U., Kim, J.: A fast perturbation algorithm using tree structure for privacy preserving utility mining. Expert Syst. Appl. 42, 1149–1165 (2015)
Acknowledgments
This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF No. 20152062051 and NRF No. 20155054624) and the Business for Academic-industrial Cooperative establishments funded Korea Small and Medium Business Administration in 2015 (Grant no. C0261068).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yun, U., Kim, D. (2017). Analysis of Privacy Preserving Approaches in High Utility Pattern Mining. In: Park, J., Pan, Y., Yi, G., Loia, V. (eds) Advances in Computer Science and Ubiquitous Computing. UCAWSN CUTE CSA 2016 2016 2016. Lecture Notes in Electrical Engineering, vol 421. Springer, Singapore. https://doi.org/10.1007/978-981-10-3023-9_137
Download citation
DOI: https://doi.org/10.1007/978-981-10-3023-9_137
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3022-2
Online ISBN: 978-981-10-3023-9
eBook Packages: EngineeringEngineering (R0)