Abstract
Multiplex graph analysis occupies a prominent position in many real-world applications, such as commodity recommendation, marketing and pandemic tracking. Due to the existence of untrusted data curators, it is in urgent need to design decentralized privacy mechanisms for analyzing multiplex graphs. Local differential privacy(LDP) is an emerging technique for preserving decentralized private data, which has drawn a great deal of attention from academic and industrial fields. The potential of LDP has been proved in various graph analysis tasks in recent researches. However, existing LDP studies may result in insufficient privacy preservation and heavy computation burden for multiplex graphs. In this paper, we introduce an eclectic privacy definition for multiplex graphs. Under this definition, we propose a randomized mechanism, called RALL, to estimate clustering coefficient of multiplex graphs with lower computation cost and higher protection strength. Furthermore, we present a post-processing method to improve the estimation accuracy. The effectiveness and efficiency of RALL mechanism are validated through extensive experiments.
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 subscriptionsReferences
Al-garadi, M., Khan, M., Varathan, K.D., Mujtaba, G., Al-Kabsi, A.M.: Using online social networks to track a pandemic: a systematic review. J. Biomed. Inf. 62, 1–11 (2016)
Blocki, J., Blum, A., Datta, A., Sheffet, O.: The johnson-lindenstrauss transform itself preserves differential privacy. In: 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science, pp. 410–419 (2012)
Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2010)
Criado, R., Flores, J., Amo, A.G.D., Gómez-Gardeñes, J., Romance, M.: A mathematical model for networks with structures in the mesoscale. Int. J. Comput. Math. 89, 291–309 (2012)
Duchi, J.C., Jordan, M.I., Wainwright, M.J.: Local privacy and statistical minimax rates. 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton), p. 1592 (2013)
Dwork, C., McSherry, F., Nissim, K., Smith, A.D.: Calibrating noise to sensitivity in private data analysis. In: TCC (2006)
Kairouz, P., et al.: Extremal mechanisms for local differential privacy. Adv. Neural Inf. Process. Syst. 27, 2879–2887 (2014)
Kasiviswanathan, S., Nissim, K., Raskhodnikova, S., Smith, A.D.: Analyzing graphs with node differential privacy. In: TCC (2013)
Qin, Z., Yu, T., Yang, Y., Khalil, I.M., Xiao, X., Ren, K.: Generating synthetic decentralized social graphs with local differential privacy. In: ACM Conference on Computer and Communications Security (2017)
Sun, H., et al.: Analyzing subgraph statistics from extended local views with decentralized differential privacy. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pp. 703–717 (2019)
Wang, Z., Liao, J., Cao, Q., Qi, H., Wang, Z.: Friendbook: a semantic-based friend recommendation system for social networks. IEEE Trans. Mob. Comput. 14, 538–551 (2015)
Warner, S.: Randomized response: a survey technique for eliminating evasive answer bias. J. Am. Stat. Assoc. 60(309), 63–6 (1965)
Wei, C., Ji, S., Liu, C., Chen, W., Wang, T.: Asgldp: Collecting and generating decentralized attributed graphs with local differential privacy. IEEE Trans. Inf. Forensics Secur. 15, 3239–3254 (2020)
Ye, Q., Hu, H., Au, M.H., Meng, X., Xiao, X.: Lf-gdpr: a framework for estimating graph metrics with local differential privacy. IEEE Trans. Knowl. Data Eng. p. 1 (2020). https://doi.org/10.1109/TKDE.2020.3047124
Ye, Q., Hu, H., Au, M., Meng, X., Xiao, X.: Towards locally differentially private generic graph metric estimation. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp. 1922–1925 (2020)
Acknowledgements
This research of Liu, Huang, Xu, Yang and Wang is partially supported by the National Science Foundation of China (NSFC) under Grants 61822210, U1709217, and 61936015; by Anhui Initiative in Quantum Information Technologies under No. AHY150300.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, Z., Xu, H., Huang, L., Yang, W. (2021). Estimating Clustering Coefficient of Multiplex Graphs with Local Differential Privacy. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12939. Springer, Cham. https://doi.org/10.1007/978-3-030-86137-7_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-86137-7_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86136-0
Online ISBN: 978-3-030-86137-7
eBook Packages: Computer ScienceComputer Science (R0)