Abstract
Privacy preserving frequent itemset mining (PPFIM) on uncertain data is booming with the increasing attention to data privacy. Existing methods use a filter function satisfies exponential differential privacy to obtain top-k frequent itemsets, and add Laplace noise to their supports to achieve the privacy protection release of top-k frequent itemsets. The privacy protection mechanism is independent of the mining process, resulting in the accuracy of the top-k frequent itemsets being affected by the k value. When the algorithm is applied to large-scale frequent itemsets, balance the data availability and privacy security is difficult. In view of the above deficiencies, the privacy protection mechanism and the mining process are integrated to design a PPFIM strategy, achieve the separation of noise addition and top-k filtering, and avoid the dependence of the algorithm accuracy on the k value. A candidate level information extraction strategy is designed to reduce the search space and effectively reduce the privacy budget by utilizing the feature of upper limit threshold in uncertain data sets. On this basis, a novel algorithm Uncertain difference privacy level frequent itemset mining (UDP-LFIM) is proposed. Theoretical analysis and experimental demonstrate that the top-k itemsets published by the algorithm can guarantee the accuracy on the premise of satisfying differential privacy.
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
Tong, Y., et al.: Mining frequent itemsets over uncertain databases. Proc. VLDB Endow. 5(11), 1650–1661 (2012). https://doi.org/10.14778/2350229.2350277
Leung, C.K.-S., et al.: Privacy-preserving frequent pattern mining from big uncertain data. In: 2018 IEEE International Conference on Big Data (Big Data) (2018). https://doi.org/10.1109/BigData.2018.8622260
Ding, Z., et al.: Frequent itemsets mining for uncertain data based on differential privacy. Appl. Res. Comput. 35(321.07), 28–32 (2018). (Chinese). https://doi.org/10.3969/j.issn.1001-3695.2018.07.004
Chui, C.-K., Kao, B., Hung, E.: Mining frequent itemsets from uncertain data. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 47–58. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-71701-0_8
Leung, C.K.S., Mateo, M.A.F., Brajczuk, D.A.: A tree-based approach for frequent pattern mining from uncertain data. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 653–661. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68125-0_61
Leung, C.K.S., Tanbeer, S.K.: Fast tree-based mining of frequent itemsets from uncertain data. In: Lee, S., Peng, Z., Zhou, X., Moon, Y.S., Unland, R., Yoo, J. (eds.) DASFAA 2012. LNCS, vol. 7238, pp. 272–287. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29038-1_21
Leung, C.K.S., Tanbeer, S.K.: PUF-tree: a compact tree structure for frequent pattern mining of uncertain data. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS (LNAI), vol. 7818, pp. 13–25. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37453-1_2
Dwork, C.: A firm foundation for private data analysis. Commun. ACM 54(1), 86–95 (2011). https://doi.org/10.1145/1866739.1866758
Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006). https://doi.org/10.1007/11681878_14
Dwork, C.: Differential privacy: a survey of results. In: Agrawal, M., Du, D., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79228-4_1
Wu, J., Ni, W., Zhang, S.: Generalization based privacy-preserving provenance publishing. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds.) WISA 2018. LNCS, vol. 11242, pp. 287–299. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02934-0_27
Lyu, M., Su, D., Li, N.: Understanding the sparse vector technique for differential privacy. Proc. VLDB Endow. 10(6), 637–648 (2017). https://doi.org/10.14778/3055330.3055331
Zhang, X., et al.: An accurate method for mining top-k frequent pattern under differential privacy. J. Integr. Plant Biol. 51(1), 104–114 (2014). (Chinese). https://doi.org/10.7544/issn1000-1239.2014.20130685
Gan, W., et al.: Frequent pattern mining with differential privacy based on transaction truncation. J. Chin. Comput. Syst. 36(11), 2583–2587 (2015). (Chinese)
Liang, W., Chen, H., Zhang, J., Zhao, D., Li, C.: An effective scheme for top-k frequent itemset mining under differential privacy conditions. Sci. China Inf. Sci. 63(5), 1–3 (2020). https://doi.org/10.1007/s11432-018-9849-y
Xiong, X., et al.: Frequent itemsets mining with differential privacy over large-scale data. IEEE Access 6, 2887 (2018). https://doi.org/10.1109/ACCESS.2018.2839752
Acknowledgement
This work is supported by the National Natural Science Foundation of China (No. 61772131), the State Grid Corporation of China Project (5700-202018268A-0-0-00).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zou, Y., Bao, X., Xu, C., Ni, W. (2020). Top-k Frequent Itemsets Publication of Uncertain Data Based on Differential Privacy. In: Wang, G., Lin, X., Hendler, J., Song, W., Xu, Z., Liu, G. (eds) Web Information Systems and Applications. WISA 2020. Lecture Notes in Computer Science(), vol 12432. Springer, Cham. https://doi.org/10.1007/978-3-030-60029-7_49
Download citation
DOI: https://doi.org/10.1007/978-3-030-60029-7_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60028-0
Online ISBN: 978-3-030-60029-7
eBook Packages: Computer ScienceComputer Science (R0)
