With the development of mobile devices, mobile crowdsourcing has become the research hotspot in mobile crowd sensing networks (MCSS). How to protect the location privacy of mobile user in location-based services (LBS) is a key problem in MCSS. However, with the increase of privacy-preserving level, the service quality will be influenced and decrease. In order to prevent mobile user’s location privacy from being leaked, this paper proposes a location privacy-preserving mechanism CKD through combining k-anonymity and differential privacy-preserving. In addition, the tradeoff between privacy protection and service quality is solved based on Stackelberg game. Through comparison experiments, the proposed location privacy-preserving CKD is verified. In addition, the tradeoff between privacy protection and service quality can be solved by our location privacy-preserving.

Capacity scaling laws of wireless networks have attracted a lot of attention. In this paper, we study the multicast capacity of bus-assistant VANETs (vehicular ad hoc networks) with two-hop relay scheme, which has not been addressed before. Assume that n ordinary vehicles and n _b buses are deployed in a grid-like road framework while the number of roads increase linearly with n. All the ordinary vehicles obey the restricted mobility model. Thus, the spatial stationary distribution decays as power law with the distance from the centre spot (home-point) of a restrict region of each vehicle. All the buses deployed in all roads as intermediate nodes. They are used to forward packets for ordinary vehicles. Each ordinary vehicle randomly chooses k - 1 vehicles from the other ordinary vehicles as receivers. The packets could be transmitted directly from source to destination or be transmitted to an intermediate vehicle or bus, then be forwarded to the destination. We found that the social-proximity urban bus-assisted VANET has three routing methods. For each routing method, we derive the matching asymptotic upper and lower bounds of multicast capacity of bus-assisted VANET.

The opioid abuse epidemic represents a major public health threat to global populations. The role social media may play in facilitating illicit drug trade is largely unknown due to limited research. However, it is known that social media use among adults in the US is widespread, there is vast capability for online promotion of illegal drugs with delayed or limited deterrence of such messaging, and further, general commercial sale applications provide safeguards for transactions; however, they do not discriminate between legal and illegal sale transactions. These characteristics of the social media environment present challenges to surveillance which is needed for advancing knowledge of online drug markets and the role they play in the drug abuse and overdose deaths. In this paper, we present a computational framework developed to automatically detect illicit drug ads and communities of vendors.The SVM- and CNNbased methods for detecting illicit drug ads, and a matrix factorization based method for discovering overlapping communities have been extensively validated on the large dataset collected from Google+, Flickr and Tumblr. Pilot test results demonstrate that our computational methods can effectively identify illicit drug ads and detect vendor-community with accuracy. These methods hold promise to advance scientific knowledge surrounding the role social media may play in perpetuating the drug abuse epidemic.