Abstract
With the raising popularity of online/mobile social applications, many individuals are increasingly attracted to their relative positions when compared to others in terms of emotional mood, travelling location, walking distance, fitness status, etc. These interest can be summarized as one question “where am I in my community?”. However, it often forms a deadlock that people are interested in the others’ data but are unwilling to disclose their own information (mood, health, etc.).
In order to break the deadlock, we propose a privacy preserving participatory sensing scheme that will not disclose individual’s privacy. Specifically, we present a privacy preservation data gathering approach and adopt an improved data mining algorithm to acquire a polynomial approximation function model on distributed user data to provide a privacy preservation method in participatory sensing. Experiments demonstrate that our approach can achieve a valid result comparing with the result without privacy preservation.
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References
Agrawal, R., Srikant, R.: Privacy-preserving data mining. ACM SIGMOD Rec. 29(2), 439–450 (2000)
He, W., Liu, X., Nguyen, H., Nahrstedt, K., Abdelzaher, T.: PDA: privacy-preserving data aggregation in wireless sensor networks. In: 26th IEEE International Conference on Computer Communications, INFOCOM 2007, pp. 2045–2053. IEEE (2007)
Rahman, F., Hoque, E., Ahamed, S.I.: Preserving privacy in wireless sensor networks using reliable data aggregation. ACM SIGAPP Appl. Comput. Rev. 11(3), 52–62 (2011)
Feng, T., Wang, C., Zhang, W., Ruan, L.: Confidentiality protection for distributed sensor data aggregation. In: The 27th Conference on Computer Communications, INFOCOM 2008. IEEE (2008)
Castelluccia, C., Mykletun, E., Tsudik, G.: Efficient aggregation of encrypted data in wireless sensor networks. In: The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, MobiQuitous 2005, pp. 109–117. IEEE (2005)
Girao, J., Westhoff, D., Schneider, M.: CDA: concealed data aggregation for reverse multicast traffic in wireless sensor networks. In: 2005 IEEE International Conference on Communications, ICC 2005, vol. 5, pp. 3044–3049. IEEE (2005)
Groat, M.M., He, W., Forrest, S.: Kipda: k-indistinguishable privacy-preserving data aggregation in wireless sensor networks. In: 2011 Proceedings IEEE INFOCOM, pp. 2024–2032. IEEE (2011)
Zhang, W., Wang, C., Feng, T.: Gp\(^{2}\)s: generic privacy-preservation solutions for approximate aggregation of sensor data (concise contribution). In: Sixth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2008, pp. 179–184. IEEE (2008)
Chan, H., Perrig, A., Song, D.: Secure hierarchical in-network aggregation in sensor networks. In: Proceedings of the 13th ACM Conference on Computer and Communications Security, pp. 278–287. ACM (2006)
http://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption
http://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength
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Tan, L. et al. (2016). Mining Myself in the Community: Privacy Preserved Crowd Sensing and Computing. In: Yang, Q., Yu, W., Challal, Y. (eds) Wireless Algorithms, Systems, and Applications. WASA 2016. Lecture Notes in Computer Science(), vol 9798. Springer, Cham. https://doi.org/10.1007/978-3-319-42836-9_25
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DOI: https://doi.org/10.1007/978-3-319-42836-9_25
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