Maintaining the Balance between Privacy and Data Integrity in Internet of Things
Author:
Affiliation:
1. Department of Computer and Information Sciences, Fordham University, New York, USA
2. College of Computer Science, and Technology, Huaqiao University, Xiamen, Fujian, China
Publisher
ACM Press
Reference10 articles.
1. Miao, C., Jiang, W., Su, L., Li, Y., Guo, S., Qin, Z., Xiao, H., Gao, J., and Ren. K. 2015. Cloud-Enabled Privacy-Preserving Truth Discovery in Crowd Sensing Systems. In Proc. of ACM SenSys, 2015.
2. Bhuiyan, M. Z. A., and Wu, J. 2016. Event Detection through Differential Pattern Mining in Internet of Things. In Proc. of IEEE MASS 2016.
3. Bhuiyan, M., and Wu, J. 2016. Trustworthy and Protected Data Collection for Event Detection Using Networked Sensing Systems. IEEE Sarnoff, 2016.
4. Li, Q., Li, Y., Gao, J., Zhao, B., Fan, W., and Han. J. 2014. Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation. In Proc. of ACM SIGMOD, 2014.
5. Ma, F., Li, Y., Li, Q., Qiu, M., Gao, J., Zhi, S., Su, L., Zhao, B., Ji, H. and Han, J. 2015. Faitcrowd: Fine grained truth discovery for crowdsourced data aggregation. In Proc. of ACM SIGKDD, 2015.
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