Federated edge learning (FEEL) is an advanced paradigm in edge artificial intelligence, enabling privacy-preserving collaborative model training through periodic communication between edge devices and a central server. FEEL involves three key processes: 1) sensing; 2) computation; and 3) communication for data acquisition, processing, and exchange, respectively. Due to limited system resources, optimizing each process individually may lead to suboptimal learning performance. This challenge has sparked research into integrated sensing-computation-communication (ISCC) design for enhanced FEEL. While previous work has optimized general learning parameters, such as batch size and computing frequency, there is a lack of customized designs considering the neural network architecture as an optimizable variable in ISCC for FEEL. To close this gap, we introduce a novel design where each device generates a submodel through controllable weight dropout, adding flexibility by directly manipulating the learning process and reducing computation and communication overhead. To guide ISCC resource allocation in this new setting, we present a comprehensive convergence analysis, revealing the tight coupling of sensing, computation, and communication across devices and their impact on FEEL convergence. Building on these theoretical insights, we formulate an ISCC problem aiming to maximize the FEEL convergence rate through joint optimization of variables, such as batch size, sensing power, dropout rate, and communication power. This nonconvex problem is decomposed into two subproblems via alternating optimization: one controls batch size using a sorting algorithm, while the other focuses on ISCC device parameters, transformable into a convex problem solved by successive convex approximation. Extensive experiments using human motion recognition datasets demonstrate the superiority of the proposed design over baseline schemes.

Since the security is an important issue in integrated sensing and communications (ISAC), especially when the targets also act as potential eavesdroppers, we investigate the secure beamforming design for the reconfigurable intelligent surface (RIS)-aided ISAC system. Aiming at minimizing the worst-case Cram & eacute;r-Rao bound of the two-dimensional direction-of-arrival estimation of the eavesdropper, we consider the joint design of transmit beamforming and RIS phase shifts subject to the constraints of signal-to-interference-plus-noise ratio (SINR) of each user and the worst-case eavesdropping SINR. To solve this joint optimization problem, we propose an algorithm based on penalty dual decomposition. Numerical results demonstrate the effectiveness of the proposed algorithm.

In this paper, we investigate the secure beamforming design for the reconfigurable intelligent surface (RIS) assisted integrated sensing and communications (ISAC) system. We consider a challenging scenario where the target act as a potential eavesdropper and the line-of-sight path between the target and the base station (BS) is blocked by the obstacles. Aiming at maximizing the secrecy rate of the system, we consider the joint design of transmit beamforming and RIS phase shift subject to the Cramer-Rao bound (CRB) constraint on the direction-of-arrival estimation of the eavesdropper. To solve this joint optimization problem, we propose an algorithm based on deep deterministic policy gradient (DDPG), by introducing a modified reward mechanism to satisfy the CRB constraint. Compared with the traditional algorithms, DDPG is simple and versatile to various environments. Numerical results demonstrate the effectiveness of the proposed algorithm and represent the trade-off relationship between communications and sensing.

Edge-device co-inference, which concerns the cooperation between edge devices and an edge server for completing inference tasks over wireless networks, has been a promising technique for enabling various kinds of intelligent services at the network edge, e.g., auto-driving. In this paradigm, the concerned design objective of the network shifts from the traditional communication throughput to the effective and efficient execution of the inference task underpinned by the network, measured by, e.g., the inference accuracy and latency. In this paper, a task-oriented over-the-air computation scheme is proposed for a multi-device artificial intelligence system. Particularly, a novel tractable inference accuracy metric is proposed for classification tasks, which is called minimum pair-wise discriminant gain. Unlike prior work measuring the average of all class pairs in feature space, it measures the minimum distance of all class pairs. By maximizing the minimum pair-wise discriminant gain instead of its average counterpart, any pair of classes can be better separated in the feature space, and thus leading to a balanced and improved inference accuracy for all classes. Besides, this paper jointly optimizes the minimum discriminant gain of all feature elements instead of separately maximizing that of each element in the existing designs. As a result, the transmit power can be adaptively allocated to the feature elements according to their different contributions to the inference accuracy, opening an extra degree of freedom to improve inference performance. Extensive experiments are conducted using a concrete use case of human motion recognition to verify the superiority of the proposed design over the benchmarking scheme.

Sensing, computation, and communication (SC 2 ) are highly coupled processes in federated edge learning (FEEL) and need to be jointly designed in a task-oriented manner for pursuing the best FEEL performance under the stringent resource constraints at edge devices. However, this remains an open problem as there is a lack of theoretical understanding on how the SC 2 resources jointly affect the FEEL performance. In this paper, we address the problem of joint SC 2 resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. Specifically, the joint SC 2 resource allocation problem is cast to maximize the convergence speed of FEEL, under the constraints on training time and energy supply of each edge device. Solving this problem entails solving two subproblems in order: the first one reduces to determining a joint sensing and communication resource allocation that maximizes the total number of samples sensed during the entire training process; the second one concerns the partition of the total number of sensed samples over communication rounds to determine the batch size at each round for convergence speed maximization. Finally, extensive simulation results are provided to validate the superiority of the proposed scheme over several baseline schemes.

With the breakthroughs in deep learning and contactless sensors, the recent years have witnessed a rise of ambient intelligence applications and services, spanning from healthcare delivery to intelligent home. Federated edge learning (FEEL), as a privacy-enhancing paradigm of collaborative learning at the network edge, is expected to be the core engine to achieve ambient intelligence. Sensing, computation, and communication (SC $^{2}$ ) are highly coupled processes in FEEL and need to be jointly designed in a task-oriented manner to achieve the best FEEL performance under stringent resource constraints at edge devices. However, this remains an open problem as there is a lack of theoretical understanding on how the SC $^{2}$ resources jointly affect the FEEL performance. In this paper, we address the problem of joint SC $^{2}$ resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing in ambient intelligence. First, by analyzing the wireless sensing process in human motion recognition, we find that there exists a thresholding value for the sensing transmit power, exceeding which yields sensing data samples with approximately the same satisfactory quality. Then, the joint SC $^{2}$ resource allocation problem is cast to maximize the convergence speed of FEEL, under the constraints on training time, energy supply, and sensing quality of each edge device. Solving this problem entails solving two subproblems in order: the first one reduces to determine the joint sensing and communication resource allocation that maximizes the total number of samples that can be sensed during the entire training process; the second one concerns the partition of the attained total number of sensed samples over all the communication rounds to determine the batch size at each round for convergence speed maximization. The first subproblem on joint sensing and communication resource allocation is converted to a single-variable optimization problem by exploiting the derived relation between different control variables (resources), which thus allows an efficient solution via one-dimensional grid search. For the second subproblem, it is found that the number of samples to be sensed (or batch size) at each round is a decreasing function of the loss function value attained at the round. Based on this relationship, the approximate optimal batch size at each communication round is derived in closed-form as a function of the round index. Finally, extensive simulation results are provided to validate the superiority of the proposed joint SC $^{2}$ resource allocation scheme over baseline schemes in terms of FEEL performance.

As cloud services have become popular, and their adoption is growing, consumers are becoming more concerned about the cost of cloud services. Cloud Service Providers (CSPs) generally use a pay-per-use billing scheme in the cloud services model: consumers use resources as they needed and are billed for their resource usage. However, CSPs are untrusted and privileged; they have full control of the entire operating system (OS) and may tamper with bills to cheat consumers. So, how to provide a trusted solution that can keep track of and verify the consumers’ resource usage has been a challenging problem. In this article, we propose a T-Counter framework based on Intel SGX. The T-Counter allows applications to construct a trusted solution to measure its CPU usage by itself in cloud computing. These constructed applications are instrumented with counters in basic blocks and added three components in trusted parts to count instructions and defend against malicious CSPs’ manipulations. We propose two algorithms which selectively instrument counters in the CFG. T-Counter is implemented as an extension of the LLVM framework and integrated with the SGX SDK. Theoretical analyses and evaluations show that T-Counter can effectively measure CPU usage and defend against malicious CSPs’ manipulations.

With the widely application of cloud, a series of privacy challenges arise. Generally, encryption methods are used to ensure privacy, which may result in high computation and communication overheads. Access control is another fundamental and important measure to protect resources. Usually cloud computing systems are managed through RESTful web services and users can conduct access control measures like role-based access control (RBAC) to manage the permissions to RESTful resources. By running integration test, test cases and the corresponding RESTful permissions can be parsed out automatically. We are the first to define the role engineering problem based on integration test and summarize three metrics for role engineering. Then we propose a novel role engineering method based on spectral clustering analysis which supporting more feature set such as permission weight, role hierarchy and customized number of roles. Finally, we conduct experiments using real integration test on three cloud computing systems to demonstrate the effectiveness and performance, outperforming prior works.

Nowadays, data privacy is one of the most critical concerns in cloud computing, and many privacy-preserving distributed computing systems based on the trusted execution environment (e.g., Intel SGX) have been proposed to protect the user’s privacy during cloud-outsourced computation. However, these SGX-based solutions are vulnerable to some traffic analyses, and loading all tasks into the enclave introduces much overhead for frequent EPC-paging. In this article, we propose a T-SGX framework, which keeps the confidentiality of a distributed job and guarantees the system efficiency by allowing dynamically loading an enclave shared object for the task under processing. In T-SGX, all these objects are secretly shared and stored in a verifiably distributed share management system (SMS) outside the TCB. To mitigate the exposure of sensitive information, we present an efficient oblivious transfer (OT) protocol under the Decisional Diffie-Hellman (DDH) assumption for obliviously transmitting desired shares. Detailed security analysis demonstrates that the proposed T-SGX achieves the goal of secure distributed computing without privacy leakage to unauthorized parties. Finally, we benchmark the framework in six real-world applications, and the experimental results show that T-SGX significantly outperforms a state-of-the-art solution, with 11.9%-29.7% less overhead performing an SGX-based application.

In this paper, the training time minimization problem is investigated in a quantized FEEL system, where the heterogeneous edge devices send quantized gradients to the edge server via orthogonal channels. In particular, a stochastic quantization scheme is adopted for compression of uploaded gradients, which can reduce the burden of per-round communication but may come at the cost of increasing number of communication rounds. The intrinsic trade-off between the number of communication rounds and per-round latency is characterized. Specifically, we analyze the convergence behavior of the quantized FEEL in terms of the optimality gap. Constrained by total bandwidth, the training time minimization problem is formulated as a joint quantization level and bandwidth allocation optimization problem. To this end, an algorithm based on alternating optimization is proposed, which alternatively solves the subproblem of quantization optimization via successive convex approximation and the subproblem of bandwidth allocation via bisection search. With different learning tasks and models, the validation of our analysis and the near-optimal performance of the proposed algorithm are demonstrated by the experimental results.

This letter studies a vertical federated edge learning (FEEL) system for collaborative objects/human motion recognition by exploiting the distributed integrated sensing and communication (ISAC). In this system, distributed edge devices first send wireless signals to sense targeted objects/human, and then exchange intermediate computed vectors (instead of raw sensing data) for collaborative recognition while preserving data privacy. To boost the spectrum and hardware utilization efficiency for FEEL, we exploit ISAC for both target sensing and data exchange, by employing dedicated frequency-modulated continuous-wave (FMCW) signals at each edge device. Under this setup, we propose a vertical FEEL scheme for realizing the recognition based on the collected multi-view wireless sensing data. In this scheme, each edge device owns an individual local L-model to transform its sensing data into an intermediate vector with relatively low dimensions, which is then transmitted to a coordinating edge device for final output via a common downstream S-model. By considering a human motion recognition task, experimental results show that our vertical FEEL based approach achieves recognition accuracy up to 98% with an improvement up to 8% compared to the benchmarks, including on-device training and horizontal FEEL.

We present ScriptChecker, a novel browser-based framework to effectively and efficiently restrict third-party script execution according to the host web page's directives.Different from all existing schemes functioning at the JavaScript layer, ScriptChecker holistically harnesses context separation and the browser's security monitors to enforce on-demand access controls upon tasks executing untrusted code.The host page can flexibly assign resource-access capabilities to tasks upon their creation.Reaping the benefits of the task capability approach, ScriptChecker outperforms existing techniques in security, usability and performance.We have implemented a prototype of ScriptChecker on Chrome and rigorously evaluated its security against 1373 malicious scripts and its usability with empirical studies upon top-1000 sites.The experimental results show that its strong security strength and ease-of-use are attained at the cost of unnoticeable performance loss.It incurs about 0.2 microseconds overhead to mediate a DOM access, and 5% delay when loading popular JS graphics and utility libraries. I. INTRODUCTIONThird-party scripts provide ready-to-use functions for specialized utilities (e.g., jQuery [15] and CryptoJS [13]) and Web feature enrichment (e.g., Yahoo Partner Ads [19] for advertisement and userreport [18] for user tracking).According to Nikiforakis et al. [44], 88.45% of web sites include at least one third-party script.They are greatly attractive to web page developers thanks to the remarkable saving in implementation.However, it has been known for years that invoking a third-party script function potentially invites unintended or even malicious code execution with the full privileges to access all resources [44], [37], [36], [33], [53].The third-party script security problem is challenging for two reasons.First, a host script and a third-party script are colocated in the same host frame and therefore have the same origin and frame identity.Thus, frame oriented or origin oriented policies and mechanisms, e.g., the same-origin policy [16], iframe isolation [6], the feature-policy header [20], and CSP [11], are unable to differentiate and handle them separately.It is hence necessary for a solution to draw the boundary

本文简要介绍了导脉冲信号的形成原理并提出了一种小型化、低功耗、高衰减动态的导脉冲组件.经测试,该组件功耗约1w,衰减动态高达100dB,精度误差≤5dB,尺寸仅为57mm×35mm×9mm,重量约30g,能够满足日益增长的高度综合化,小型化,低功耗的航电系统的设计需求.

Constrained reinforcement learning (CRL), also termed as safe reinforcement learning, is a promising technique enabling the deployment of RL agent in real-world systems. In this paper, we propose a successive convex approximation based off-policy optimization (SCAOPO) algorithm to solve the general CRL problem, which is formulated as a constrained Markov decision process (CMDP) in context of the average cost. The SCAOPO is based on solving a sequence of convex objective/feasibility optimization problems obtained by replacing the objective and constraint functions in the original problem with convex surrogate functions. The proposed SCAOPO enables reuse of experiences from previous updates, thereby significantly reducing implementation cost when deployed in real-world engineering systems that need to online learn the environment. In spite of the time-varying state distribution and the stochastic bias incurred by off-policy learning, the SCAOPO with a feasible initial point can still provably converge to a Karush-Kuhn-Tucker (KKT) point of the original problem almost surely.

Training a machine learning model with federated edge learning (FEEL) is typically time consuming due to the constrained computation power of edge devices and the limited wireless resources in edge networks. In this study, the training time minimization problem is investigated in a quantized FEEL system, where heterogeneous edge devices send quantized gradients to the edge server via orthogonal channels. In particular, a stochastic quantization scheme is adopted for compression of uploaded gradients, which can reduce the burden of per-round communication but may come at the cost of increasing the number of communication rounds. The training time is modeled by taking into account the communication time, computation time, and the number of communication rounds. Based on the proposed training time model, the intrinsic trade-off between the number of communication rounds and per-round latency is characterized. Specifically, we analyze the convergence behavior of the quantized FEEL in terms of the optimality gap. Furthermore, a joint data-and-model-driven fitting method is proposed to obtain the exact optimality gap, based on which the closed-form expressions for the number of communication rounds and the total training time are obtained. Constrained by the total bandwidth, the training time minimization problem is formulated as a joint quantization level and bandwidth allocation optimization problem. To this end, an algorithm based on alternating optimization is proposed, which alternatively solves the subproblem of quantization optimization through successive convex approximation and the subproblem of bandwidth allocation by bisection search. With different learning tasks and models, the validation of our analysis and the near-optimal performance of the proposed optimization algorithm are demonstrated by the simulation results.

随着云计算和移动计算的普及,浏览器应用呈现多样化和规模化的特点,浏览器的安全问题也日益突出.为了保证Web应用资源的安全性,浏览器同源策略被提出.目前,RFC6454、W3C和HTML5标准都对同源策略进行了描述与定义,诸如Chrome、Fire fox、Safari、Edge等主流浏览器均将其作为基本的访问控制策略.然而,浏览器同源策略在实际应用中面临着无法处理第三方脚本引入的安全威胁、无法限制同源不同frame的权限、与其他浏览器机制协作时还会为不同源的frame赋予过多权限等问题,并且无法保证跨域/跨源通信机制的安全性以及内存攻击下的同源策略安全.对浏览器同源策略安全研究进行综述,介绍了同源策略的规则,并概括了同源策略的威胁模型与研究方向,主要包括同源策略规则不足及应对、跨域与跨源通信机制安全威胁及应对以及内存攻击下的同源策略安全,并且展望了同源策略安全研究的未来发展方向.

We design a joint radio and computational resource allocation policy for a multi-user mobile edge computing system, such that the expected power consumption is minimized while satisfying long-term delay constraints. The problem is formulated as a constrained Markov decision process (CMDP) that is efficiently solved by the proposed constrained reinforcement learning (CRL) algorithm, called successive convex programming based policy optimization (SCPPO). SCPPO solves a convex objective/feasibility surrogate problem at each update and it can provably converge to a Karush-Kuhn-Tucker (KKT) point of the original CMDP problem almost surely under some mild conditions. Moreover, SCPPO adopts an application-specific policy architecture and employs a data-efficient estimation strategy that can reuse old experiences, such that SCPPO can realize fast learning with low computational complexity.

We investigate the downlink transmission for multi-user multi-input multi-out (MU-MIMO) system, in which the regularized zero forcing (RZF) precoder is adopted and the power allocation and regularization factor are optimized. Our aim is to find a power allocation and regularization factor control policy that can minimize the long-term average power consumption subject to long-term delay constraint for each user. The induced optimization problem is formulated as a constrained Markov decision process (CMDP), which is efficiently solved by the proposed constrained deep reinforcement learning algorithm, called successive convex approximation policy optimization (SCAPO). The SCAPO is based on solving a sequence of convex objective/feasibility optimization problems obtained by replacing the objective and constraint functions in the original problems with convex surrogate functions. At each iteration, the SCAPO merely needs to estimate the first-order information and solve a convex surrogate problem that can be efficiently parallel tackled. Moreover, the SCAPO enables to reuse old experiences from previous updates, thereby significantly reducing the implementation cost. Numerical results have shown that the novel SCAPO can achieve the state-of-the-art performance over advanced baselines.

安全与可信是云计算中极为重要的需求,如何保护用户在云平台上托管的应用程序代码和数据的安全、防止云服务提供商和其他攻击者窃取用户机密数据,一直是个难题.2013年,Intel公司提出了新的处理器安全技术SGX,能够在计算平台上提供一个用户空间的可信执行环境,保证用户关键代码及数据的机密性和完整性.SGX技术自提出以来,已成为云计算安全问题的重要解决方案.如何有效地应用SGX技术来保护用户的应用程序,成为近年来的研究热点.介绍了SGX的相关机制和SDK,概括了SGX应用所面临的安全问题、性能瓶颈问题、开发困难问题和功能局限性等问题,总结并归纳了SGX应用支持技术的研究进展,包括SGX应用安全防护技术、SGX应用性能优化技术、SGX应用辅助开发技术和SGX功能扩展技术.并展望了未来的发展方向.

Container technology has been used for running multiple isolated operating system distros on a host or deploying large scale microservice-based applications. In most cases, containers share the same kernel with the host and other containers on the same host, and the application in the container can make system calls of the host kernel like a normal process on the host. Seccomp is a security mechanism for the Linux kernel, through which we can prohibit certain system calls from being executed by the program. Docker began to support the seccomp mechanism from version 1.10 and disables around 44 system calls out of 300+ by default. However, for a particular container, there are still many system calls that are unnecessary for running it allowed to be executed, and the abuse of system calls by a compromised container can trigger the security vulnerabilities of a host kernel. Unfortunately, Docker does not provide a way to get the necessary system calls for a particular container. In this paper, we propose RSDS, a method combining dynamic analysis and static analysis to get the necessary system calls for a particular container. Our experiments show that our solution can reduce system calls by 69.27%-85.89% compared to the default configuration on an x86-64 PC with Ubuntu 16.04 host OS and does not affect the functionalities of these containers.