It is of significant importance to provide incentives to smartphone users in mobile crowd sensing systems. Recently, a number of auction-based incentive mechanisms have been proposed. However, an auction-based incentive mechanism may unexpectedly release the location privacy of smartphone users, which may seriously reduce the willingness of users participating in contributing sensing data. In an auction-based incentive mechanism, even if the location of a user is not enclosed in his/her bid submitted to the platform, the location information may still be inferred by an adversary by using the prices of the tasks required by the user. We take an example to show how an attack can recover the location information of a smartphone user by merely knowing his/her bid. To defend against such an attack, we propose a method to protect location privacy in auctions for mobile crowd sensing systems. This method encrypts prices in a bid so that the adversary cannot access and hence the location privacy of users can be protected. In the meanwhile, however, the auction can proceed properly, i.e. the platform can select the user offering the lowest price for each sensing task or the platform can choose users with budget constraint. We demonstrate the effectiveness of our proposed method with theoretical analysis and simulations.

BACKGROUND: RNA binding proteins play important roles in post-transcriptional RNA processing and transcriptional regulation. Distinguishing the RNA-binding residues in proteins is crucial for understanding how protein and RNA recognize each other and function together as a complex. RESULTS: We propose PredRBR, an effectively computational approach to predict RNA-binding residues. PredRBR is built with gradient tree boosting and an optimal feature set selected from a large number of sequence and structure characteristics and two categories of structural neighborhood properties. In cross-validation experiments on the RBP170 data set show that PredRBR achieves an overall accuracy of 0.84, a sensitivity of 0.85, MCC of 0.55 and AUC of 0.92, which are significantly better than that of other widely used machine learning algorithms such as Support Vector Machine, Random Forest, and Adaboost. We further calculate the feature importance of different feature categories and find that structural neighborhood characteristics are critical in the recognization of RNA binding residues. Also, PredRBR yields significantly better prediction accuracy on an independent test set (RBP101) in comparison with other state-of-the-art methods. CONCLUSIONS: The superior performance over existing RNA-binding residue prediction methods indicates the importance of the gradient tree boosting algorithm combined with the optimal selected features.

It is of significant importance to provide incentives to smartphone users in mobile crowd sensing systems. And a number of auction-based incentive mechanisms have been proposed. However, an auction-based incentive mechanism may unexpectedly release the location privacy of smartphone users, which may seriously reduce users' willingness of participating in mobile crowd sensing. In an auction-based mechanism, even if the location of the user is not enclosed in its bid submitted to the platform, the location information may still be inferred by an adversary by using the prices of the tasks required by the user. We show such an attack on a typical auction-based incentive mechanism and reveal that the attack can recover the location information of a smartphone user by merely knowing the bid from the user. To defend against such an attack, we propose P2, a location privacy-preserving auction mechanism for mobile crowd sensing systems. This mechanism encrypts prices in a bid so that the adversary cannot access and hence the location privacy of the user can be protected. In the meanwhile, however, the auction can proceed properly, i.e. the platform can select the user offering the lowest price for each sensing task. We demonstrate the effectiveness of our the proposed mechanism with simulation experiments.

Network coding in space, a new direction also named space information flow, is verified to have potential advantages over routing in space if the geometric conditions are satisfied. Cost advantage is adopted to measure the performance for network coding in space. Present literatures proved that only in regular (5 + 1) model, network coding in space is strictly superior to routing in terms of single-source multicast, comparing with other regular (n + 1) models. Focusing on irregular (5 + 1) model, this paper uses geometry to quantitatively study the constructions of network coding and optimal routing when a sink node moves without limits in space. Furthermore, the upperbound of cost advantage is figured out as well as the region where network coding is strictly superior to routing. Some properties of network coding in space are also presented.