轻量级移动支付协议LMPP的安全性分析与改进

无线网络以及各种智能设备的兴起,使得移动支付越来越重要,因此必须采用安全的轻量级移动支付协议（Lightweight Mobile Payment Protocol,LMPP）来保障移动支付的顺利进行。针对资源有限的移动设备及环境受限的支付场景,选取以移动运营商（Mobile Network Operator,MNO）为价值链的轻量级的隐私保护移动支付协议为研究对象。采用SVO逻辑形式化分析协议,证明协议不满足公平性。并对LMPP协议其他安全属性进行分析,针对协议不满足公平性的部分做出改进。同时运用SPIN模型检测工具进行检测,验证结果表明,改进后的协议满足公平性。     

On proportional fairness of uplink spectral efficiency in cell‐free massive MIMO systems

This paper is concerned with the proportional fairness (PF) of the spectral efficiency (SE) maximization of uplinks in a cell‐free (CF) massive multiple‐input multiple‐output (MIMO) system in which a large number of single‐antenna access points (APs) connected to a central processing unit (CPU) serve many single‐antenna users. To detect the user signals, the APs use matched filters based on the local channel state information while the CPU deploys receiver filters based on knowledge of channel statistics. We devise the maximization problem of the SE PF, which maximizes the sum of the logarithm of the achievable user rates, as a jointly nonconvex optimization problem of receiver filter coefficients and user power allocation subject to user power constraints. To handle the challenges associated with the nonconvexity of the formulated design problem, we develop an iterative algorithm by alternatively finding optimal filter coefficients at the CPU and transmit powers at the users. While the filter coefficient design is formulated as a generalized eigenvalue problem, the power allocation problem is addressed by a gradient projection (GP) approach. Simulation results show that the SE PF maximization not only offers approximately the achievable sum rates as compared to the sum‐rate maximization but also provides an improved trade‐off between the user rate fairness and the achievable sum rate.     

Proportional Fairness-Based Power Allocation Algorithm for Downlink NOMA 5G Wireless Networks

Non-orthogonal multiple access (NOMA) is one of the key 5G technology which can improve spectrum efficiency and increase the number of user connections by utilizing the resources in a non-orthogonal manner. NOMA allows multiple terminals to share the same resource unit at the same time. The receiver usually needs to configure successive interference cancellation (SIC). The receiver eliminates co-channel interference (CCI) between users and it can significantly improve the system throughput. In order to meet the demands of users and improve fairness among them, this paper proposes a new power allocation scheme. The objective is to maximize user fairness by deploying the least fairness in multiplexed users. However, the objective function obtained is non-convex which is converted into convex form by utilizing the optimal Karush-Kuhn-Tucker (KKT) constraints. Simulation results show that the proposed power allocation scheme gives better performance than the existing schemes which indicates the effectiveness of the proposed scheme.     

Sum Rate Maximization-based Fair Power Allocation in Downlink NOMA Networks

Non-orthogonal multiple access (NOMA) has been seen as a promising technology for 5G communication. The performance optimization of NOMA systems depends on both power allocation (PA) and user pairing (UP). Most existing researches provide sub-optimal solutions with high computational complexity for PA problem and mainly focuses on maximizing the sum rate (capacity) without considering the fairness performance. Also, the joint optimization of PA and UP needs an exhaustive search. The main contribution of this paper is the proposing of a novel capacity maximization-based fair power allocation (CMFPA) with low-complexity in downlink NOMA. Extensive investigation and analysis of the joint impact of signal to noise ratio (SNR) per subcarrier and the channel gains of the paired users on the performance of NOMA in terms of the capacity and the user fairness is presented. Next, a closed-form equation for the power allocation coefficient of CMFPA as a function of SNR, and the channel gains of the paired users is provided. In addition, to jointly optimize UP and PA in NOMA systems an efficient low-complexity UP (ELCUP) method is proposed to be incorporated with the proposed CMFPA to compromise the proposed joint resource allocation (JRA). Simulation results demonstrate that the proposed CMFPA can improve the capacity and fairness performance of existing UP methods, such as conventional UP, and random UP methods. Furthermore, the simulation results show that the proposed JRA significantly outperforms the existing schemes and gives a near-optimal performance.     

基于以太坊区块链的公平可搜索加密方案

传统的对称可搜索加密解决了云存储中加密数据的检索问题,但是没有考虑到检索的公平性问题,即用户在支付了服务费后服务器没有返回检索结果或返回错误的检索结果的情况。随着区块链的出现,基于比特币的对称可搜索加密方案被提出,但是比特币系统的交易周期长,且比特币的脚本语言不是图灵完备的,不能适用于更多的场景。因此提出基于以太坊区块链和智能合约的对称可搜索加密方案,在保证数据隐私性的同时,解决了检索的公平性问题。安全性和性能分析结果表明该方案是可行的。     

公平性机器学习研究综述

随着机器学习在社会中的广泛使用,带来的歧视问题引发广泛的社会争议,这逐步引起了产业界和学术界对机器学习算法公平性问题的浓厚兴趣。目前对公平性度量和机器学习公平性机制的研究仍然处于初级阶段。本文对公平性机器学习的研究进行了调研,首先从公平性的定义出发,比较了衡量公平性指标的方法,然后调研了公平性数据集,对公平性问题的产生进行了分析,接下来对现有的公平性机器学习算法进行归类和比较,最后总结了当前公平性机器学习研究中存在的问题,并对关键问题和重大挑战进行了讨论。     

BBR拥塞控制算法延迟及带宽探测优化

传统基于丢包的拥塞控制算法因为其高丢包率和引发缓冲区膨胀问题已经不能满足许多应用对网络性能的要求。谷歌提出的BBR（Bottleneck Bandwidth and Round Trip）算法以其抗丢包、高带宽利用率和低延迟等特性受到广泛关注与研究。但是BBR还存在排队延迟仍然较高、在RTT（Round Trip Time）较小环境下表现不佳、带宽探测不及时等问题。本文对BBR排队延迟和收敛性进行分析,进而提出改进方法：限制在外数据包数,并根据网络反馈适时减少拥塞窗口大小来降低延迟;在RTT较小环境下,将探测RTT阶段之前的带宽估计延续到探测RTT阶段之后;设置平稳状态最长保持时间及时退出平稳周期并进入探测周期。在NS3中的仿真实验结果表明,改进BBR降低了RTT及其抖动,提高了算法的收敛速度;能够在RTT较小环境下高效利用带宽;改进BBR能够显著提高长RTT流的带宽探测频率。     

Distributed congestion control strategy using network utility maximization theory in VANET

Cooperative vehicle safety system (CVSS) rely on periodical beacons to track neighboring vehicles.High traffic density often causes channel congestion,seriously damaging the performance of CVSS.Existing congestion control strategies aim to ensure the performance in network layer,without considering the service requirements of vehicles in different driving contexts.To solve the problem,a distributed congestion control strategy using network utility maximization (NUM) theory was proposed.First of all,the NUM model for channel resource allocation was introduced.A utility function reflecting vehicle’s safety requirements was proposed in the model.Then under the condition of fixed transmit powers,a optimization problem of channel resource allocation was proposed.Lastly,to solve the optimization problem,a distributed congestion control algorithm named utility-based rate congestion control (UBRCC) algorithm was designed,the algorithm worked out the optimal beaconing rate by updating vehicle’s congestion price,realizing the resource allocation according to vehicle’s safety requirements.Simulation results validate that UBRCC algorithm can efficiently control channel congestion,reduce transmission delay,ensure reliable data transmission and satisfies the requirements of safety applications.     

轻量级移动支付协议公平性分析

为保证移动支付安全、顺利进行,必须采用安全的移动支付协议。针对计算和存储能力有限的移动设备和不可靠的移动环境,选择采用对称加密的轻量级移动支付协议PCMS,使用串空间理论对其建模,进行形式化分析。通过图的方式直观描述协议的执行过程,分析协议安全目标,基于串空间理论的认证测试方法,对该协议的公平性形式化分析。针对PCMS协议不满足公平性,提出增加时间戳来解决,同时增加一个退款子协议完成后续退款操作。结合模型检测工具验证分析,结果表明,改进后的协议满足公平性。     

考虑公平性的面向多灾点需求应急资源调度

针对铁路突发事件多需求点多供应点的应急资源调度问题,结合"软时间窗"的概念,以公平性最大和调度总成本最小为优化目标,设计了有多个救援目标的应急资源调度模型,并利用并列选择遗传算法求解。该算法根据目标函数的个数,将种群均等地划分为与目标函数个数相等的子种群,为划分后的各个子种群各自分配一个目标函数,并对其进行独立的选择运算,将各个子种群中适应度高的个体组成新的种群,对这个新的种群进行交叉、变异,生成下一代种群。算例表明,与粒子群优化（PSO）和两阶段启发式算法相比,利用并列选择遗传算法进行计算,目标函数中所有需求点的资源满足程度的方差分别减小了93.88%、89.88%,成本分别减少了5%、0.15%。所提算法能够有效减小所有需求点的资源满足程度的方差,即提高各需求点的公平性,同时降低成本,其在求解多目标规划问题中能够得到更优的解。