报告题目：Privacy Preservation and Incentive Mechanism in Crowdsensing
Data privacy preservation has drawn great research attention recently. In this tutorial, we will first consider the problem of constructing marginal tables given a set of participant's multi-dimensional data while satisfying Local Differential Privacy (LDP), presenting several results on single-attribute data. We then introduce CALM, Consistent Adaptive Local Marginal, that takes advantage of the careful challenge analysis and performs consistently better than existing methods. Further, we focues on how to design efficient incentive mechanisms to encourage participants' involvement. We propose an efficient incentive mechanism named REAP to reconcile the aggregation accuracy and participants' data privacy. The proposed incentive mechanism offers different contracts to participants with different privacy preferences, by which fusion center can directly control them. Experimental as well as simulation results are provided to validate the feasibility and advantages of our proposed algorithms.
报告题目：Cross-domain Mobile Users’ Behavior Modelling and Prediction
By focusing on characterizing the mobile traffic, web and information usage traces based on large-scale and long-duration mobile big data, which is collected from the commercial mobile operator with more than 10 thousand base stations and 6.5 million users spanning over some months, we qualitatively visualize and quantitatively characterize the spatio-temporal human behaviors in the physical-cyber space in terms of mobility, traffic consumption, social activity, etc. Based on these fundamental findings and credible models, we further investigate how to utilize these important insights to deal with problems encountered with the current mobile networks, urban management, and robust cyber-physical systems.