Abstract
Mobile sensing mines group information through sensing and aggregating users’ data. Among major mobile sensing applications, the distinct counting problem aiming to find the number of distinct elements in a data stream with repeated elements, is extremely important for avoiding waste of resources. Besides, the privacy protection of users is also a critical issue for aggregation security. However, it is a challenge to meet these two requirements simultaneously since normal privacy-preserving methods would have negative influence on the accuracy and efficiency of distinct counting. In this paper, we propose a Privacy-preserving Distinct Counting Scheme (PDCS) for mobile sensing. By integrating the basic idea of homomorphic encryption into Flajolet-Martin (FM) sketch, PDCS allows an aggregator to conduct distinct counting over large-scale data sets without knowing privacy of users. Moreover, PDCS supports various forms of sensing data, including camera images, location data, etc. PDCS expands each bit of the hashing values of users’ original data, FM sketch is thus enhanced for encryption to protect users’ privacy. We prove the security of PDCS under known-plaintext model. The theoretic and experimental results show that PDCS achieves high counting accuracy and practical efficiency with scalability over large-scale data sets.
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References
Au, M.H., Liang, K., Liu, J.K., Lu, R., Ning, J.: Privacy-preserving personal data operation on mobile cloud—chances and challenges over advanced persistent threat. Future Gener. Comput. Syst. 79, 337–349 (2018)
Bae, M., Kim, K., Kim, H.: Preserving privacy and efficiency in data communication and aggregation for AMI network. J. Netw. Comput. Appl. 59, 333–344 (2016)
Bar-Yossef, Z., Jayram, T.S., Kumar, R., Sivakumar, D., Trevisan, L.: Counting distinct elements in a data stream. In: Rolim, J.D.P., Vadhan, S. (eds.) RANDOM 2002. LNCS, vol. 2483, pp. 1–10. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45726-7_1
Considine, J., Li, F., Kollios, G., Byers, J.: Approximate aggregation techniques for sensor databases. In: Proceedings of the 20th International Conference on Data Engineering, pp. 449–460. IEEE (2004)
Dietzel, S., Bako, B., Schoch, E., Kargl, F.: A fuzzy logic based approach for structure-free aggregation in vehicular ad-hoc networks. In: Proceedings of the Sixth ACM International Workshop on VehiculAr InterNETworking, pp. 79–88. ACM (2009)
Flajolet, P., Martin, G.N.: Probabilistic counting algorithms for data base applications. J. Comput. Syst. Sci. 31(2), 182–209 (1985)
Garofalakis, M., Hellerstein, J.M., Maniatis, P.: Proof sketches: verifiable in-network aggregation. In: IEEE 23rd International Conference on Data Engineering, ICDE 2007, pp. 996–1005. IEEE (2007)
Han, Q., Du, S., Ren, D., Zhu, H.: SAS: a secure data aggregation scheme in vehicular sensing networks. In: 2010 IEEE International Conference on Communications (ICC), pp. 1–5. IEEE (2010)
Kirschenhofer, P., Prodinger, H., Szpankowski, W.: How to count quickly and accurately: a unified analysis of probabilistic counting and other related problems. In: Kuich, W. (ed.) ICALP 1992. LNCS, vol. 623, pp. 211–222. Springer, Heidelberg (1992). https://doi.org/10.1007/3-540-55719-9_75
Li, Q., Cao, G.: Efficient and privacy-preserving data aggregation in mobile sensing. In: 2012 20th IEEE International Conference on Network Protocols (ICNP), pp. 1–10. IEEE (2012)
Li, Q., Cao, G., La Porta, T.F.: Efficient and privacy-aware data aggregation in mobile sensing. IEEE Trans. Dependable Secure Comput. 11(2), 115–129 (2014)
Lochert, C., Rybicki, J., Scheuermann, B., Mauve, M.: Scalable data dissemination for inter-vehicle-communication: aggregation versus peer-to-peer (skalierbare informationsverbreitung für die fahrzeug-fahrzeug-kommunikation: aggregation versus peer-to-peer). IT-Inf. Technol. 50(4), 237–242 (2008)
Lochert, C., Scheuermann, B., Mauve, M.: Probabilistic aggregation for data dissemination in VANETs. In: Proceedings of the Fourth ACM International Workshop on Vehicular ad Hoc Networks, pp. 1–8. ACM (2007)
Lochert, C., Scheuermann, B., Mauve, M.: A probabilistic method for cooperative hierarchical aggregation of data in VANETs. Ad Hoc Netw. 8(5), 518–530 (2010)
Lu, R., Liang, X., Li, X., Lin, X., Shen, X.: EPPA: an efficient and privacy-preserving aggregation scheme for secure smart grid communications. IEEE Trans. Parallel Distrib. Syst. 23(9), 1621–1631 (2012)
Ma, R., Cao, Z.: Serial number based encryption and its application for mobile social networks. Peer-to-Peer Netw. Appl. 10(2), 332–339 (2017)
Nadeem, T., Dashtinezhad, S., Liao, C., Iftode, L.: TrafficView: traffic data dissemination using car-to-car communication. ACM SIGMOBILE Mob. Comput. Commun. Rev. 8(3), 6–19 (2004)
Rana, R.K., Chou, C.T., Kanhere, S.S., Bulusu, N., Hu, W.: Ear-phone: an end-to-end participatory urban noise mapping system. In: Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, pp. 105–116. ACM (2010)
Samanthula, B.K., Jiang, W., Madria, S.: A probabilistic encryption based min/max computation in wireless sensor networks. In: 2013 IEEE 14th International Conference on Mobile Data Management (MDM), vol. 1, pp. 77–86. IEEE (2013)
Scheuermann, B., Mauve, M.: Near-optimal compression of probabilistic counting sketches for networking applications. In: DIALM-POMC. Citeseer (2007)
Tan, X., et al.: An autonomous robotic fish for mobile sensing. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5424–5429. IEEE (2006)
Tao, Y., Kollios, G., Considine, J., Li, F., Papadias, D.: Spatio-temporal aggregation using sketches. In: Proceedings of the 20th International Conference on Data Engineering, pp. 214–225. IEEE (2004)
Thiagarajan, A., et al.: VTrack: accurate, energy-aware road traffic delay estimation using mobile phones. In: Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, pp. 85–98. ACM (2009)
Wang, L., et al.: Fine-grained probability counting: refined loglog algorithm. In: IEEE Bigcomp (2018)
Zhang, Y., Chen, Q., Zhong, S.: Efficient and privacy-preserving min and \( k \) th min computations in mobile sensing systems. IEEE Trans. Dependable Secure Comput. 14(1), 9–21 (2017)
Acknowledgement
This work is supported in part by the National Natural Science Foundation of China (NSFC) under grants 61872372, 61572026, 61672195, and Open Foundation of State Key Laboratory of Cryptology (No:MMKFKT201617).
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Yang, X., Xu, M., Fu, S., Luo, Y. (2019). PDCS: A Privacy-Preserving Distinct Counting Scheme for Mobile Sensing. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11446. Springer, Cham. https://doi.org/10.1007/978-3-030-18576-3_14
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