Abstract
With the advancements of sensing technologies, participatory data fusion and analysis have raised more and more attention as it provides a promising way enabling the public benefit from this process. However, it is increasingly becoming a challenging problem about how to construct a statistical model for this kind of particular phenomenon under the premise of protecting the data privacy. In this paper, we present a method to build a linear regression model describing a phenomenon observed in the distributed network. At the same time, the private data of each node is preserved.
We propose a data aggregation algorithm to fuse the indispensable data for constructing linear regression equation even though the data is private and scattered in the whole network. We also point out that the aggregate node can not only conduct regression modeling, but can conduct some complex statistical analyses based on the aggregation result as well. In addition, we investigate the ability and the degree of the algorithm to preserve the data privacy. We mainly focus on the ability to protect the aggregation result of a community composed of sensor nodes. The experiment shows that the aggregation result can not be disclosed to people unless all of the sensor nodes in the community collude with each other.
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References
Ahmadi, H., Pham, N., Ganti, R., Abdelzaher, T., Nath, S., Han, J.: Privacy-aware regression modeling of participatory sensing data. In: Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems. pp. 99–112. ACM (2010)
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)
Chen, X., Makki, K., Yen, K., Pissinou, N.: Sensor network security: a survey. IEEE Commun. Surv. Tutorials 11(2), 52–73 (2009)
Ding, M., Cheng, X., Xue, G.: Aggregation tree construction in sensor networks. In: 2003 IEEE 58th Vehicular Technology Conference VTC 2003-Fall, vol. 4, pp. 2168–2172. IEEE (2003)
Du, W., Han, Y., Chen, S.: Privacy-preserving multivariate statistical analysis: Linear regression and classification. In: Proceedings of the 4th SIAM International Conference on Data Mining. vol. 233. Lake Buena Vista, Florida (2004)
Feng, T., Wang, C., Zhang, W., Ruan, L.: Confidentiality protection for distributed sensor data aggregation. In: The 27th IEEE Conference on Computer Communications INFOCOM 2008 IEEE (2008)
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)
He, W., Nguyen, H., Liu, X., Nahrstedt, K., Abdelzaher, T.: IPDA: an integrity-protecting private data aggregation scheme for wireless sensor networks. In: IEEE Military Communications Conference MILCOM 2008, pp. 1–7. IEEE (2008)
L, T.: http://www.liaad.up.pt/ltorgo/regression/datasets.html
Li, N., Zhang, N., Das, S.K., Thuraisingham, B.: Privacy preservation in wireless sensor networks: a state-of-the-art survey. Ad Hoc Netw. 7(8), 1501–1514 (2009)
Powell, M.: An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput. J. 7(2), 155–162 (1964)
Sanil, A., Karr, A., Lin, X., Reiter, J.: Privacy preserving regression modelling via distributed computation. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery And Data Mining, pp. 677–682. ACM (2004)
Shi, J., Zhang, R., Liu, Y., Zhang, Y.: Prisense: privacy-preserving data aggregation in people-centric urban sensing systems. In: 2010 IEEE Proceedings of INFOCOM, pp. 1–9. IEEE (2010)
Xing, K., Wan, Z., Hu, P., Zhu, H., Wang, Y., Chen, X., Wang, Y., Huang, L.: Mutual privacy-preserving regression modeling in participatory sensing. In: 2013 IEEE Proceedings of INFOCOM, pp. 3039–3047. IEEE (2013)
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Wu, J., Zhang, F. (2015). Privacy-Preserving Regression Modeling and Attack Analysis in Sensor Network. In: Qiang, W., Zheng, X., Hsu, CH. (eds) Cloud Computing and Big Data. CloudCom-Asia 2015. Lecture Notes in Computer Science(), vol 9106. Springer, Cham. https://doi.org/10.1007/978-3-319-28430-9_27
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DOI: https://doi.org/10.1007/978-3-319-28430-9_27
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