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With the advancement of sensor-embedded mobile electronic devices, participatory sensing has attracted more and more attention because it can collect data, analyze information and outsource tasks to those devices' users. However, range query results obtained at the requesters and dissemination data produced at the providers are usually sensitive and private to be disclosed, e.g., users' locations,...
Density estimation can construct an estimate of the probability density function from the observed data. However, such a function may compromise the privacy of individuals. A notable paradigm for offering strong privacy guarantees in data analysis is differential privacy. In this paper, we propose DPGMM, a parametric density estimation algorithm using Gaussian mixtures model (GMM) under differential...
Mobile devices have become more and more powerful with lots of embedded sensors providing sensibility of various data, which makes crowd sensing a compelling paradigm followed by emerging applications. In this paper, we design a crowd based incentive mechanism to stimulate participants joining sensing campaigns with consideration of location privacy. In our incentive, MagiCrowd, we creatively cluster...
Wireless sensor network is an important part of the Internet of Things. Preservation of privacy and integrity in wireless sensor networks is extremely urgent and challenging. To address this problem, we propose in this paper an efficient and secure range query protocol in two-tiered wireless sensor networks introducing master nodes. Our proposal not only prevents adversaries from gaining sensitive...
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