Location Trace Privacy Under Conditional Priors
Providing meaningful privacy to users of location based services is particularly challenging when multiple locations are revealed in a short period of time. This is primarily due to the tremendous degree of dependence that can be anticipated between points. We propose a R\'enyi divergence based privacy framework for bounding expected privacy loss for conditionally dependent data. Additionally, we demonstrate an algorithm for achieving this privacy under Gaussian process conditional priors. This framework both exemplifies why conditionally dependent data is so challenging to protect and offers a strategy for preserving privacy to within a fixed radius for sensitive locations in a user's trace.
Updated: 2021-02-23 21:55:34
标题: Location Trace Privacy Under Conditional Priors （条件先验下的位置追踪隐私）
摘要: 在短时间内揭示多个位置时，为位置基础服务的用户提供有意义的隐私保护尤为具有挑战性。这主要是由于可以预期的点之间的巨大依赖程度。我们提出了一个基于R\'enyi散度的隐私框架，用于限制条件相关数据的预期隐私损失。此外，我们展示了一种算法，可在高斯过程条件先验下实现此隐私保护。这个框架既说明了为什么条件相关数据如此难以保护，又提供了一种在用户轨迹中的敏感位置内保持隐私在固定半径内的策略。
更新时间: 2021-02-23 21:55:34
领域: cs.AI,cs.CR
Usability and Security of Different Authentication Methods for an Electronic Health Records System
We conducted a survey of 67 graduate students enrolled in the Privacy and Security in Healthcare course at Indiana University Purdue University Indianapolis. This was done to measure user preference and their understanding of usability and security of three different Electronic Health Records authentication methods: single authentication method (username and password), Single sign-on with Central Authentication Service (CAS) authentication method, and a bio-capsule facial authentication method. This research aims to explore the relationship between security and usability, and measure the effect of perceived security on usability in these three aforementioned authentication methods. We developed a formative-formative Partial Least Square Structural Equation Modeling (PLS-SEM) model to measure the relationship between the latent variables of Usability, and Security. The measurement model was developed using five observed variables (measures). - Efficiency and Effectiveness, Satisfaction, Preference, Concerns, and Confidence. The results obtained highlight the importance and impact of these measures on the latent variables and the relationship among the latent variables. From the PLS-SEM analysis, it was found that security has a positive impact on usability for Single sign-on and bio-capsule facial authentication methods. We conclude that the facial authentication method was the most secure and usable among the three authentication methods. Further, descriptive analysis was done to draw out the interesting findings from the survey regarding the observed variables.
Updated: 2021-02-23 18:29:26
标题: 不同认证方法对电子健康记录系统的可用性和安全性
摘要: 我们对印第安纳大学普渡大学印第安纳波利斯校区的隐私与医疗保健安全课程中的67名研究生进行了调查。这是为了衡量用户对三种不同的电子健康记录认证方法的偏好和理解程度：单一认证方法（用户名和密码）、使用中央认证服务（CAS）认证方法的单一登录，以及生物胶囊面部认证方法。这项研究旨在探讨安全性和可用性之间的关系，并衡量在这三种认证方法中被感知为安全性对可用性的影响。我们开发了一个形成-形成偏最小二乘结构方程模型（PLS-SEM）来衡量可用性和安全性之间的潜在变量之间的关系。测量模型使用了五个观察变量（测量）-效率和有效性、满意度、偏好、关注和信心。获得的结果突出了这些测量对潜在变量及其之间关系的重要性和影响。通过PLS-SEM分析，发现安全性对于单一登录和生物胶囊面部认证方法的可用性有积极影响。我们得出结论认为，面部认证方法是这三种认证方法中最安全和可用的。此外，还进行了描述性分析，以从调查中的观察变量中获得有趣的发现。
更新时间: 2021-02-23 18:29:26
领域: cs.CR
Enhancing Certified Robustness via Smoothed Weighted Ensembling
Randomized smoothing has achieved state-of-the-art certified robustness against $l_2$-norm adversarial attacks. However, it is not wholly resolved on how to find the optimal base classifier for randomized smoothing. In this work, we employ a Smoothed WEighted ENsembling (SWEEN) scheme to improve the performance of randomized smoothed classifiers. We show the ensembling generality that SWEEN can help achieve optimal certified robustness. Furthermore, theoretical analysis proves that the optimal SWEEN model can be obtained from training under mild assumptions. We also develop an adaptive prediction algorithm to reduce the prediction and certification cost of SWEEN models. Extensive experiments show that SWEEN models outperform the upper envelope of their corresponding candidate models by a large margin. Moreover, SWEEN models constructed using a few small models can achieve comparable performance to a single large model with a notable reduction in training time.
Updated: 2021-02-23 14:03:58
标题: 通过平滑加权集成增强认证鲁棒性
摘要: 随机平滑技术已经在$l_2$-范数对抗攻击下取得了最先进的认证强度。然而，如何找到最佳的基本分类器以实现随机平滑仍然没有完全解决。在这项工作中，我们采用了一种平滑加权集成（SWEEN）方案来提高随机平滑分类器的性能。我们展示了集成的普适性，即SWEEN可以帮助实现最佳认证强度。此外，理论分析证明，在一些温和的假设下，可以从训练中获得最佳的SWEEN模型。我们还开发了一种自适应预测算法，以降低SWEEN模型的预测和认证成本。大量实验证明，SWEEN模型的性能远远优于其相应候选模型的上界。此外，使用少量小模型构建的SWEEN模型可以在训练时间显著减少的情况下实现与单个大模型相当的性能。
更新时间: 2021-02-23 14:03:58
领域: cs.LG,cs.CR,stat.ML
Automatic Extraction of Secrets from the Transistor Jungle using Laser-Assisted Side-Channel Attacks
The security of modern electronic devices relies on secret keys stored on secure hardware modules as the root-of-trust (RoT). Extracting those keys would break the security of the entire system. As shown before, sophisticated side-channel analysis (SCA) attacks, using chip failure analysis (FA) techniques, can extract data from on-chip memory cells. However, since the chip's layout is unknown to the adversary in practice, secret key localization and reverse engineering are onerous tasks. Consequently, hardware vendors commonly believe that the ever-growing physical complexity of the integrated circuit (IC) designs can be a natural barrier against potential adversaries. In this work, we present a novel approach that can extract the secret key without any knowledge of the IC's layout, and independent from the employed memory technology as key storage. We automate the -- traditionally very labor-intensive -- reverse engineering and data extraction process. To that end, we demonstrate that black-box measurements captured using laser-assisted SCA techniques from a training device with known key can be used to profile the device for a later key prediction on other victim devices with unknown keys. To showcase the potential of our approach, we target keys on three different hardware platforms, which are utilized as RoT in different products.
Updated: 2021-02-23 12:23:46
标题: 激光辅助侧信道攻击自晶体管丛林中自动提取密码
摘要: 现代电子设备的安全性依赖于存储在安全硬件模块上的秘密密钥作为信任根源(RoT)。提取这些密钥将破坏整个系统的安全性。如先前所示，使用芯片故障分析(FA)技术进行复杂的侧信道分析(SCA)攻击可以从芯片上的存储器单元中提取数据。然而，在实践中，由于对手对芯片的布局是未知的，秘密密钥的定位和逆向工程是繁重的任务。因此，硬件供应商普遍认为集成电路(IC)设计不断增长的物理复杂性可以成为潜在对手的自然屏障。在这项工作中，我们提出了一种新的方法，可以在不了解IC布局的情况下提取秘密密钥，并且独立于所采用的存储技术。我们自动化传统上非常费力的逆向工程和数据提取过程。为此，我们展示了使用激光辅助SCA技术从具有已知密钥的训练设备中捕获的黑盒测量可以用于为以后在具有未知密钥的其他受害设备上进行密钥预测而对设备进行配置。为展示我们方法的潜力，我们针对三种不同的硬件平台上的密钥，这些平台被用作不同产品中的RoT。
更新时间: 2021-02-23 12:23:46
领域: cs.CR
V2W-BERT: A Framework for Effective Hierarchical Multiclass Classification of Software Vulnerabilities
Weaknesses in computer systems such as faults, bugs and errors in the architecture, design or implementation of software provide vulnerabilities that can be exploited by attackers to compromise the security of a system. Common Weakness Enumerations (CWE) are a hierarchically designed dictionary of software weaknesses that provide a means to understand software flaws, potential impact of their exploitation, and means to mitigate these flaws. Common Vulnerabilities and Exposures (CVE) are brief low-level descriptions that uniquely identify vulnerabilities in a specific product or protocol. Classifying or mapping of CVEs to CWEs provides a means to understand the impact and mitigate the vulnerabilities. Since manual mapping of CVEs is not a viable option, automated approaches are desirable but challenging. We present a novel Transformer-based learning framework (V2W-BERT) in this paper. By using ideas from natural language processing, link prediction and transfer learning, our method outperforms previous approaches not only for CWE instances with abundant data to train, but also rare CWE classes with little or no data to train. Our approach also shows significant improvements in using historical data to predict links for future instances of CVEs, and therefore, provides a viable approach for practical applications. Using data from MITRE and National Vulnerability Database, we achieve up to 97% prediction accuracy for randomly partitioned data and up to 94% prediction accuracy in temporally partitioned data. We believe that our work will influence the design of better methods and training models, as well as applications to solve increasingly harder problems in cybersecurity.
Updated: 2021-02-23 05:16:57
标题: V2W-BERT：一种用于软件漏洞有效层次多类分类的框架
摘要: 计算机系统中的弱点，如软件架构、设计或实现中的故障、错误和漏洞，提供了可以被攻击者利用来危害系统安全的漏洞。常见弱点枚举（CWE）是一个按层次设计的软件弱点字典，提供了理解软件缺陷、其利用可能带来的影响以及缓解这些缺陷的方法。常见漏洞和暴露（CVE）是简短的低级描述，用于唯一标识特定产品或协议中的漏洞。对CVE进行分类或映射到CWE提供了理解漏洞的影响并加以缓解的方法。由于手动映射CVE不切实际，自动化方法是可取的但具有挑战性。 本文提出了一种新颖的基于Transformer的学习框架（V2W-BERT）。通过借鉴自然语言处理、链接预测和迁移学习的思想，我们的方法不仅在具有丰富数据进行训练的CWE实例中表现出色，而且在少量或无数据进行训练的罕见CWE类别中也表现优异。我们的方法还显示出在使用历史数据预测未来CVE实例的链接时显著改进，因此，为实际应用提供了可行的方法。利用MITRE和国家漏洞数据库的数据，我们在随机分区数据中实现了高达97%的预测准确度，在时间分区数据中实现了高达94%的预测准确度。我们相信我们的工作将影响更好方法和训练模型的设计，以及解决网络安全日益困难的问题的应用。
更新时间: 2021-02-23 05:16:57
领域: cs.LG,cs.CR
Towards Activity-Centric Access Control for Smart Collaborative Ecosystems
The ubiquitous presence of smart devices along with advancements in connectivity coupled with the elastic capabilities of cloud and edge systems have nurtured and revolutionized smart ecosystems. Intelligent, integrated cyber-physical systems offer increased productivity, safety, efficiency, speed and support for data driven applications beyond imagination just a decade ago. Since several connected devices work together as a coordinated unit to ensure efficiency and automation, the individual operations they perform are often reliant on each other. Therefore, it is important to control what functions or activities different devices can perform at a particular moment of time, and how they are related to each other. It is also important to consider additional factors such as conditions, obligation or mutability of activities, which are critical in deciding whether or not a device can perform a requested activity. In this paper, we take an initial step to propose and discuss the concept of Activity-Centric Access Control (ACAC) for smart and connected ecosystem. We discuss the notion of activity with respect to the collaborative and distributed yet integrated systems and identify the different entities involved along with the important factors to make an activity control decision. We outline a preliminary approach for defining activity control expressions which can be applied to different smart objects in the system. The main goal of this paper is to present the vision and need for the activity-centric approach for access control in connected smart systems, and foster discussion on the identified future research agenda.
Updated: 2021-02-23 04:28:15
标题: 朝向智能协作生态系统的面向活动的访问控制
摘要: 智能设备的普遍存在以及连接性的进步，加上云和边缘系统的弹性能力，培育并革新了智能生态系统。智能、集成的物理系统提供了比十年前想象中更高的生产力、安全性、效率、速度和对数据驱动应用的支持。由于多个连接设备共同作为协调单元以确保效率和自动化，它们执行的个别操作经常依赖于彼此。因此，控制不同设备在特定时间内可以执行的功能或活动，以及它们之间的关系是非常重要的。还需要考虑到其他因素，如条件、活动的义务性或可变性，这些因素在决定设备是否能执行请求的活动时至关重要。在本文中，我们首次提出并讨论了智能和连接生态系统中的活动中心访问控制（ACAC）概念。我们讨论了与协作和分布式但集成系统相关的活动概念，并确定了涉及的不同实体以及做出活动控制决策所涉及的重要因素。我们概述了定义活动控制表达式的初步方法，这些表达式可以应用于系统中的不同智能对象。本文的主要目标是提出活动中心方法在连接智能系统中的访问控制的愿景和需求，并促进对确定的未来研究议程的讨论。
更新时间: 2021-02-23 04:28:15
领域: cs.CR
Bayesian Inference with Certifiable Adversarial Robustness
We consider adversarial training of deep neural networks through the lens of Bayesian learning, and present a principled framework for adversarial training of Bayesian Neural Networks (BNNs) with certifiable guarantees. We rely on techniques from constraint relaxation of non-convex optimisation problems and modify the standard cross-entropy error model to enforce posterior robustness to worst-case perturbations in $\epsilon$-balls around input points. We illustrate how the resulting framework can be combined with methods commonly employed for approximate inference of BNNs. In an empirical investigation, we demonstrate that the presented approach enables training of certifiably robust models on MNIST, FashionMNIST and CIFAR-10 and can also be beneficial for uncertainty calibration. Our method is the first to directly train certifiable BNNs, thus facilitating their deployment in safety-critical applications.
Updated: 2021-02-23 04:23:58
标题: 贝叶斯推断与可证实的对抗鲁棒性
摘要: 我们通过贝叶斯学习的视角考虑深度神经网络的敌对训练，并提出了一个有保证的框架，用于对贝叶斯神经网络（BNNs）进行敌对训练。我们依赖于对非凸优化问题的约束松弛技术，并修改了标准的交叉熵误差模型，以确保后验对输入点周围$\epsilon$-球中最坏情况的扰动具有鲁棒性。我们展示了如何将所得到的框架与通常用于BNNs的近似推断方法相结合。在实证研究中，我们证明了所提出的方法能够在MNIST、FashionMNIST和CIFAR-10上训练具有保证的鲁棒模型，并且对于不确定性校准也可能有益。我们的方法是第一个直接训练具有保证的BNNs的方法，从而促进它们在安全关键应用中的部署。
更新时间: 2021-02-23 04:23:58
领域: cs.LG,cs.CR
Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning
This paper introduces the first provably accurate algorithms for differentially private, top-down decision tree learning in the distributed setting (Balcan et al., 2012). We propose DP-TopDown, a general privacy preserving decision tree learning algorithm, and present two distributed implementations. Our first method NoisyCounts naturally extends the single machine algorithm by using the Laplace mechanism. Our second method LocalRNM significantly reduces communication and added noise by performing local optimization at each data holder. We provide the first utility guarantees for differentially private top-down decision tree learning in both the single machine and distributed settings. These guarantees show that the error of the privately-learned decision tree quickly goes to zero provided that the dataset is sufficiently large. Our extensive experiments on real datasets illustrate the trade-offs of privacy, accuracy and generalization when learning private decision trees in the distributed setting.
Updated: 2021-02-23 03:24:20
标题: 可扩展和可证明准确性的差分隐私分布式决策树学习算法
摘要: 本文介绍了在分布式环境中对差分隐私进行保护的第一个经证明准确的算法，用于自上而下的决策树学习（Balcan等，2012）。我们提出了DP-TopDown，一个通用的隐私保护决策树学习算法，并提出了两种分布式实现方法。我们的第一种方法NoisyCounts通过使用拉普拉斯机制自然地扩展了单机算法。我们的第二种方法LocalRNM通过在每个数据持有者处执行本地优化，显著减少了通信和添加的噪声。我们首次为单机和分布式环境中的差分隐私自上而下决策树学习提供了效用保证。这些保证表明，只要数据集足够大，隐私学习的决策树的错误将迅速趋于零。我们在真实数据集上进行了大量实验，展示了在分布式环境中学习私有决策树时隐私、准确性和泛化之间的权衡。
更新时间: 2021-02-23 03:24:20
领域: cs.LG,cs.CR,stat.ML
Cost-Aware Robust Tree Ensembles for Security Applications
There are various costs for attackers to manipulate the features of security classifiers. The costs are asymmetric across features and to the directions of changes, which cannot be precisely captured by existing cost models based on $L_p$-norm robustness. In this paper, we utilize such domain knowledge to increase the attack cost of evading classifiers, specifically, tree ensemble models that are widely used by security tasks. We propose a new cost modeling method to capture the feature manipulation cost as constraint, and then we integrate the cost-driven constraint into the node construction process to train robust tree ensembles. During the training process, we use the constraint to find data points that are likely to be perturbed given the feature manipulation cost, and we use a new robust training algorithm to optimize the quality of the trees. Our cost-aware training method can be applied to different types of tree ensembles, including gradient boosted decision trees and random forest models. Using Twitter spam detection as the case study, our evaluation results show that we can increase the attack cost by 10.6X compared to the baseline. Moreover, our robust training method using cost-driven constraint can achieve higher accuracy, lower false positive rate, and stronger cost-aware robustness than the state-of-the-art training method using $L_\infty$-norm cost model. Our code is available at https://github.com/surrealyz/growtrees.
Updated: 2021-02-23 02:07:27
标题: 成本感知的安全应用中的强大树集成
摘要: 攻击者操纵安全分类器特征的成本各不相同。这些成本在特征和变化方向上是不对称的，无法被现有基于$L_p$-范数鲁棒性的成本模型精确捕捉。本文利用这种领域知识，增加规避分类器攻击的成本，具体来说，是广泛用于安全任务的树集成模型。我们提出了一种新的成本建模方法来捕捉特征操纵成本作为约束，然后将成本驱动的约束集成到节点构建过程中，训练出鲁棒的树集成。在训练过程中，我们利用约束找到可能在给定特征操纵成本下被扰动的数据点，并使用新的鲁棒训练算法优化树的质量。我们的成本感知训练方法可以应用于不同类型的树集成，包括梯度提升决策树和随机森林模型。以Twitter垃圾邮件检测为案例研究，我们的评估结果显示，与基线相比，我们可以将攻击成本提高10.6倍。此外，我们的成本驱动约束下的鲁棒训练方法可以实现更高的准确率，更低的误报率，以及比使用$L_\infty$-范数成本模型的最新训练方法更强的成本感知鲁棒性。我们的代码可在https://github.com/surrealyz/growtrees找到。
更新时间: 2021-02-23 02:07:27
领域: cs.CR,cs.LG
Man-in-The-Middle Attacks and Defense in a Power System Cyber-Physical Testbed
Man-in-The-Middle (MiTM) attacks present numerous threats to a smart grid. In a MiTM attack, an intruder embeds itself within a conversation between two devices to either eavesdrop or impersonate one of the devices, making it appear to be a normal exchange of information. Thus, the intruder can perform false data injection (FDI) and false command injection (FCI) attacks that can compromise power system operations, such as state estimation, economic dispatch, and automatic generation control (AGC). Very few researchers have focused on MiTM methods that are difficult to detect within a smart grid. To address this, we are designing and implementing multi-stage MiTM intrusions in an emulation-based cyber-physical power system testbed against a large-scale synthetic grid model to demonstrate how such attacks can cause physical contingencies such as misguided operation and false measurements. MiTM intrusions create FCI, FDI, and replay attacks in this synthetic power grid. This work enables stakeholders to defend against these stealthy attacks, and we present detection mechanisms that are developed using multiple alerts from intrusion detection systems and network monitoring tools. Our contribution will enable other smart grid security researchers and industry to develop further detection mechanisms for inconspicuous MiTM attacks.
Updated: 2021-02-23 01:59:56
标题: 《电力系统网络物理试验台中的中间人攻击及防御》
摘要: 中间人攻击对智能电网构成了多种威胁。在中间人攻击中，入侵者将自己嵌入两个设备之间的对话中，以窃听或冒充其中一个设备，使其看起来像是正常的信息交流。因此，入侵者可以执行虚假数据注入（FDI）和虚假命令注入（FCI）攻击，危及电力系统运行，如状态估计、经济调度和自动发电控制（AGC）。很少有研究人员专注于在智能电网中难以检测的中间人攻击方法。为了解决这个问题，我们正在设计和实施多阶段的中间人入侵，在基于仿真的网络物理电力系统实验室中对一个大规模的合成电网模型进行测试，以展示这类攻击如何引起物理应急情况，如误操作和虚假测量。中间人入侵在这个合成电力网中创建了虚假命令注入、虚假数据注入和重放攻击。这项工作使利益相关者能够防范这些隐蔽的攻击，并且我们提供了使用来自入侵检测系统和网络监控工具的多个警报开发的检测机制。我们的贡献将使其他智能电网安全研究人员和行业能够进一步开发针对不明显的中间人攻击的检测机制。
更新时间: 2021-02-23 01:59:56
领域: cs.CR,cs.SY,eess.SY
A Survey on Amazon Alexa Attack Surfaces
Since being launched in 2014, Alexa, Amazon's versatile cloud-based voice service, is now active in over 100 million households worldwide. Alexa's user-friendly, personalized vocal experience offers customers a more natural way of interacting with cutting-edge technology by allowing the ability to directly dictate commands to the assistant. Now in the present year, the Alexa service is more accessible than ever, available on hundreds of millions of devices from not only Amazon but third-party device manufacturers. Unfortunately, that success has also been the source of concern and controversy. The success of Alexa is based on its effortless usability, but in turn, that has led to a lack of sufficient security. This paper surveys various attacks against Amazon Alexa ecosystem including attacks against the frontend voice capturing and the cloud backend voice command recognition and processing. Overall, we have identified six attack surfaces covering the lifecycle of Alexa voice interaction that spans several stages including voice data collection, transmission, processing and storage. We also discuss the potential mitigation solutions for each attack surface to better improve Alexa or other voice assistants in terms of security and privacy.
Updated: 2021-02-23 01:14:19
标题: 调查亚马逊Alexa攻击面
摘要: 自2014年推出以来，亚马逊灵活多变的基于云的语音服务Alexa在全球超过1亿家庭中都处于活跃状态。Alexa用户友好的个性化语音体验为客户提供了一种更自然的与尖端技术互动的方式，使他们能够直接向助手口头下达命令。如今，在当今这一年，Alexa服务比以往任何时候都更加便捷，不仅可以在亚马逊设备上使用，还可以在第三方设备制造商的数亿设备上使用。不幸的是，这一成功也引发了关注和争议。Alexa的成功基于其简单易用性，但反过来，这也导致了安全性不足的问题。本文调查了针对亚马逊Alexa生态系统的各种攻击，包括针对前端语音捕捉和云后端语音命令识别和处理的攻击。总体而言，我们确定了涵盖Alexa语音交互生命周期的六个攻击面，包括语音数据收集、传输、处理和存储等多个阶段。我们还讨论了针对每个攻击面的潜在缓解解决方案，以更好地提高Alexa或其他语音助手的安全性和隐私性。
更新时间: 2021-02-23 01:14:19
领域: cs.CR