Abstract
In the age of big data, plenty of valuable data have been shared to enhance scientific innovation, which, however, may disclose unexpected privacy leakage. Although numerous privacy preservation techniques have been proposed to conceal sensitive information, it is usually at the cost of the application utility reduction. In this paper, we present a data sharing scheme, which balances the application utility and privacy leakage for specific data sharing. To illustrate our scheme, smartphones’ acceleration data have been adopted as an illustrative example. Experimental study has shown that sampling frequency play dominant roles in reducing privacy leakage with much less reduction on utility.
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References
Kapadia, A., Triandopoulos, N., Cornelius, C., Peebles, D., Kotz, D.: AnonySense: opportunistic and privacy-preserving context collection. In: Indulska, J., Patterson, D.J., Rodden, T., Ott, M. (eds.) Pervasive 2008. LNCS, vol. 5013, pp. 280–297. Springer, Heidelberg (2008). doi:10.1007/978-3-540-79576-6_17
Boutsis, I., Kalogeraki, V.: Privacy preservation for participatory sensing data. In: IEEE International Conference on Pervasive Computing and Communications, pp. 103–113 (2013)
Liu, B., Jiang, Y., Sha, F., et al.: Cloud-enabled privacy-preserving collaborative learning for mobile sensing. In: Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems, pp. 57–70. ACM (2012)
Ganti, R.K., Pham, N., Tsai, Y.E., et al.: PoolView: stream privacy for grassroots participatory sensing. In: Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, pp. 281–294. ACM (2008)
Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) Pervasive 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24646-6_1
Wang, H.: Privacy-preserving data sharing in cloud computing. J. Comput. Sci. Technol. 25(3), 401–414 (2010)
Hull, R., Kumar, B., Lieuwen, D., et al.: Enabling context-aware and privacy-conscious user data sharing. In: Proceedings of the 2004 IEEE International Conference on Mobile Data Management, 2004, pp. 187–198. IEEE (2004)
Lane, N.D., Miluzzo, E., Lu, H., et al.: A survey of mobile phone sensing. IEEE Commun. Mag. 48(9), 140–150 (2010)
Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM Sigkdd Explor. Newsl. 12(2), 74–82 (2010)
Győrbíró, N., Fábián, Á., Hományi, G.: An activity recognition system for mobile phones. Mob. Netw. Appl. 14(1), 82–91 (2009)
Ailisto, H.J, Lindholm, M., Mantyjarvi, J., et al.: Identifying people from gait pattern with accelerometers. In: International Society for Optics and Photonics, Defense and Security, pp. 7–14 (2005)
Gafurov, D., Helkala, K., Søndrol, T.: Biometric gait authentication using accelerometer sensor. J. comput. 1(7), 51–59 (2006)
Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Cell phone-based biometric identification. In: 2010 Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), pp. 1–7. IEEE (2010)
Derawi, M., Bours, P.: Gait and activity recognition using commercial phones. Comput. secur. 39, 137–144 (2013)
Christin, D., Reinhardt, A., Kanhere, S.S., et al.: A survey on privacy in mobile participatory sensing applications. J. Syst. and Softw. 84(11), 1928–1946 (2011)
Cho, I.Y., Sunwoo, J., Son, Y.K., Oh, M.-H., Lee, C.-H.: Development of a single 3-axis accelerometer sensor based wearable gesture recognition band. In: Indulska, J., Ma, J., Yang, Laurence T., Ungerer, T., Cao, J. (eds.) UIC 2007. LNCS, vol. 4611, pp. 43–52. Springer, Heidelberg (2007). doi:10.1007/978-3-540-73549-6_5
Yang, C.C., Hsu, Y.L.: A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors 10(8), 7772–7788 (2010)
Kawaguchi, N., Ogawa, N., Iwasaki, Y., et al.: HASC challenge: gathering large scale human activity corpus for the real-world activity understandings. In: Proceedings of the 2nd Augmented Human International Conference, p. 27. ACM (2011)
Lockhart, J.W., Weiss, G.M.: The benefits of personalized smartphone-based activity recognition models. In: SDM, pp. 614–622 (2014)
Shoaib, M., Bosch, S., Incel, O.D., et al.: A survey of online activity recognition using mobile phones. Sensors 15(1), 2059–2085 (2015)
Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SigKDD Explor. Newsl. 12(2), 74–82 (2011)
Incel, O.D., Kose, M., Ersoy, C.: A review and taxonomy of activity recognition on mobile phones. BioNanoScience 3(2), 145–171 (2013)
Guo, B., Nixon, M.S.: Gait feature subset selection by mutual information. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 39(1), 36–46 (2009)
Avci, A., Bosch, S., Marin-Perianu, M., et al.: Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: a survey. In: 2010 23rd International Conference on Architecture of Computing Systems (ARCS), pp. 1–10. VDE (2010)
Parameswaran, R., Blough, D.M.: Privacy preserving collaborative filtering using data obfuscation. In: IEEE International Conference on Granular Computing, 2007. GRC 2007, p. 380. IEEE (2007)
Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: ACM Sigmod Record. vol. 29(2), pp. 439–450. ACM (2000)
Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(05), 557–570 (2002)
Aritra, D., Min, C., Robert, K.: Measuring privacy and utility in privacy-preserving visualization. Comput. Graph. Forum 32(32), 35–47 (2013)
Tarameshloo, E., et al.: Using visualization to explore original and anonymized LBSN data. In: Eurographics/IEEE VGTC Conference on Visualization Eurographics Association, pp. 291–300 (2016)
Wang, Y., Gou, L., Xu, A., et al.: VeilMe: an interactive visualization tool for privacy configuration of using personality traits. In: ACM Conference on Human Factors in Computing Systems. pp. 817–826. ACM (2015)
Kawaguchi, N., Ogawa, N., Iwasaki, Y., et al.: HASC Challenge: gathering large scale human activity corpus for the real-world activity understandings. In: Proceedings of the 2nd Augmented Human International Conference, p. 27. ACM (2011)
Lockhart, J.W., Weiss, G.M.: The benefits of personalized smartphone-based activity recognition models. In: SDM, pp. 614–622 (2014)
Gjoreski, H., Kozina, S., Gams, M., et al.: Competitive live evaluations of activity-recognition systems. IEEE Pervasive Comput. 14(1), 70–77 (2015)
Acknowledgement
This work was partially supported by Project Nos. 61232001, 61173169, and 60903222 supported by National Science Foundation of China, No. 2016JJ2149 supported by Science Foundation of Hunan, and also supported by the Major Science & Technology Research Program for Strategic Emerging Industry of Hunan (Grant No. 2012GK4054).
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Lu, M., Guo, Y., Meng, D., Li, C., Zhao, Y. (2017). An Information-Aware Privacy-Preserving Accelerometer Data Sharing. In: Zou, B., Li, M., Wang, H., Song, X., Xie, W., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 727. Springer, Singapore. https://doi.org/10.1007/978-981-10-6385-5_36
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DOI: https://doi.org/10.1007/978-981-10-6385-5_36
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