In password security a defender would like to identify and warn users with weak passwords. Similarly, the defender may also want to predict what fraction of passwords would be cracked within B guesses as the attacker’s guessing budget B varies from small (online attacker) to large (offline attacker). Towards each of these goals the defender would like to quickly estimate the guessing number for each user password pwd assuming that the attacker uses a password cracking model M i.e., how many password guesses will the attacker check before s/he cracks each user password pwd. Since naïve brute-force enumeration can be prohibitively expensive when the guessing number is very large, Dell’Amico and Filippone [1] developed an efficient Monte Carlo algorithm to estimate the guessing number of a given password pwd. While Dell’Amico and Filippone proved that their estimator is unbiased there is no guarantee that the Monte Carlo estimates are accurate nor does the method provide confidence ranges on the estimated guessing number or even indicate if/when there is a higher degree of uncertainty.Our contributions are as follows: First, we identify theoretical examples where, with high probability, Monte Carlo Strength estimation produces highly inaccurate estimates of individual guessing numbers as well as the entire guessing curve. Second, we introduce Confident Monte Carlo Strength Estimation as an extension of Dell’Amico and Filippone [1]. Given a password our estimator generates an upper and lower bound with the guarantee that, except with probability δ, the true guessing number lies within the given confidence range. Our techniques can also be used to characterize the attacker’s guessing curve. In particular, given a probabilistic password cracking model M we can generate high confidence upper and lower bounds on the fraction of passwords that the attacker will crack as the guessing budget B varies.

With the development of D2D (Device-to- Device) communications, the ever increasingly growing demand for content sharing caused by nearby devices can be satisfied. D2D communications based on social relationships are taken into consideration in extant literature, while how to select friends with whom users share contents efficaciously and voluntarily remains a problem. To this end, social tie, as a measurement of content similarity and mutual trust, is introduced in our work to facilitate contents sharing. In the paper, we first model friend selection problem as a finite horizon optimal stopping problem in the frame of optimal stopping theory and derive the optimal social cost threshold. We further prove the optimal social cost threshold is endowed with monotonically non- decreasing property. Moreover, we develop an iterative algorithm based on derived social cost threshold to select friends with whom users are inclined to share contents. Last but not the least, numerous simulation results validate the effectiveness of our proposed algorithm for content sharing over random D2D communications.

Autonomous vehicle industry is developing by leaps and bounds. We envisage a situation where autonomous vehicles act as taxis to transport clients. During the transportation, vehicle sharing is enabled and encouraged. We propose fair allocation in payment for passengers of a shared vehicle. The allocation is endowed with the characteristics of envy freeness and maximin utilities. Furthermore, we devise working mechanisms for autonomous vehicles, including (i) planning routes with minimum detour; (ii) choosing clients to recommend vehicle sharing; (iii) carrying passengers continuously. Our work may serve as a good preparation for the marketization of autonomous taxis.

Network tomography can help detect network link delay. End-to-end path measurement is a promising method in network tomography which could reduce measurement overhead. In end-to-end path measurement, however, deriving link states is computational complex due to the existence of massive links. Because only a few links are congested at the same time, we can locate sparsely congested links by using compressed sensing (CS). In this paper, based on expander graphs, we demonstrate that routing matrix constructed by full tree network can satisfy Restricted Isometry Property (RIP) and be used as measurement matrix. Finally, sparsely congested links are given with CS. Evaluation results show that routing matrix constructed in this paper can help achieve high link delay recovery accuracy, i.e., small recovery error by using 1i-minimization in CS. It also illustrate that the recovery error decreases with the number of congested links decrease.

With the explosive development of social networks, users' behaviors are prone to be socially connected. Social Network Operator (SNO) can effectively collect communication data which reflects users' social behaviors and construct a social graph which precisely characterize relationship among users. Users who exert online influence on their friends can potentially affect their friends' demand for wireless service. Therefore, the knowledge of users' influence would help Wireless Network Operator (WNO) make optimal operating decisions to maximize profit. This paper focus on the optimal pricing strategy of WNO when users' social influence is taken into consideration. We model SNO and WNO's relationship as a cooperative game. By formulating the interaction between SNO-WNO's coalition and users as a two-stage Stackelberg game, the optimal pricing strategy is derived which brings about extra profits. Moreover, we propose a generalized bargaining scheme based on contribution factor to divide extra profits between operators. Simulation results reveal the fact that wireless network operator's possession of influence information does incur extra profits. In addition, feasibility of SNO and WNO's cooperation is validated.