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UMBRELLA: user demand privacy preserving framework based on association rules and differential privacy in social networks

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  1. 1

    Li H X, Zhu H J, Ma D. Demographic information inference through meta-data analysis of wi-fi traffic. IEEE Trans Mobile Comput, 2018, 17: 1033–1047

  2. 2

    Li H X, Chen Q R, Zhu H J, et al. Privacy leakage via de-anonymization and aggregation in heterogeneous social networks. IEEE Trans Depen Secur Comput, 2017. doi: 10.1109/TDSC.2017.2754249

  3. 3

    Peng T, Liu Q, Wang G J. Enhanced location privacy preserving scheme in location-based services. IEEE Syst J, 2017, 11: 219–230

  4. 4

    Shahid A R, Jeukeng L, Zeng W, et al. PPVC: privacy preserving voronoi cell for location-based services. In: Proceedings of International Conference on Computing, Networking and Communications, Santa Clara, 2017. 351–355

  5. 5

    Ma X D, Li H, Ma J F, et al. APPLET: a privacypreserving framework for location-aware recommender system. Sci China Inf Sci, 2017, 60: 092101

  6. 6

    Zhu H, Lu R X, Huang C, et al. An efficient privacypreserving location-based services query scheme in outsourced cloud. IEEE Trans Veh Technol, 2016, 65: 7729–7739

  7. 7

    Andrés M E, Bordenabe N E, Chatzikokolakis K, et al. Geo-indistinguishability: differential privacy for location-based systems. In: Proceedings of the 2013 ACM SIGSAC Conference on Computer and Communications Security, Berlin, 2013. 901–914

  8. 8

    Shokri R. Privacy games: optimal user-centric data obfuscation. In: Proceedings of the 15th Privacy Enhancing Technologies, Philadelphia, 2015. 299–315

  9. 9

    Feng L, Dillon T, Liu J. Inter-transactional association rules for multi-dimensional contexts for prediction and their application to studying meteorological data. Data Knowl Eng, 2001, 37: 85–115

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This work was supported by National Natural Sciences Foundation of China (Grant No. 61501211), Basic Research Project of Shenzhen (Grant Nos. JCYJ20160531192013063, JCYJ20170307151148585), Natural Sciences Foundation of Guangdong (Grant No. 2017A030313372), Natural Scientific Research Innovation Foundation in Harbin Institute of Technology, Natural Sciences Foundation of Jiangxi (Grant Nos. 20151BAB217001, 20151BAB217018), and S&T Foundation of Jingdezhen.

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Correspondence to Bin Cao.

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Yan, C., Ni, Z., Cao, B. et al. UMBRELLA: user demand privacy preserving framework based on association rules and differential privacy in social networks. Sci. China Inf. Sci. 62, 39106 (2018).

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