The rapid proliferation of emotion-aware web services has necessitated the analysis of multimodal user interactions. However, this introduces new vulnerabilities where adversaries exploit emotional signals to circumvent fraud detection systems. Despite its improved utility, the robustness of multimodal fraud detection against emotion-driven adversarial manipulation remains significantly underexplored. Existing paradigms often treat emotional cues as static features, overlooking the adversary's capability to strategically modulate multimodal signals (e.g., facial micro-expressions, vocal intonation, and textual styles) to mimic genuine behavior. Furthermore, prevalent evaluations are typically confined to unimodal perturbations and fail to account for context-consistent, cross-modal attacks, thereby compromising system reliability in real-world deployments. To bridge this gap, we propose Context-Emotion Adversarial Training (CEAT), a robust framework designed to fortify multimodal fraud detection against emotion-based attacks. CEAT leverages a Transformer-based architecture to synergistically model emotional features (e.g., visual dynamics and acoustic prosody) alongside semantic context derived from text, yielding a unified representation. Crucially, CEAT introduces a context-aware perturbation mechanism that injects noise into the emotional latent space during training. This process preserves semantic consistency while encouraging the learning of emotion-invariant and discriminative representations. Additionally, a contrastive learning objective is integrated to maximize the distributional divergence between genuine and adversarial samples within the latent manifold. Extensive experiments on multimodal benchmarks demonstrate that CEAT significantly outperforms state-of-the-art baselines, exhibiting superior robustness under simulated emotion-driven attack scenarios.

Modern cloud applications, from interative web services to mobile and WoT workloads, generate highly dynamic multi-tenant network demands. Guaranteeing priority-aware bandwidth remains challenging: legacy shapers like Linux Traffic Control Hierarchical Token Bucket are static and unscalable, while cloud-native solutions such as Cilium offer only coarse-grained rate limiting. We present BeeQoS, a cloud-native QoS system that delivers low-latency, adaptive, and scalable multi-priority bandwidth guarantees. BeeQoS consists of an eBPF-powered data plane for high-performance, fine-grained per-packet shaping, a demand-aware control plane that senses real-time flow requirements and adaptively reallocates bandwidth, and seamless Kubernetes integration for expressive policy specification and cluster-wide scalable deployment. Evaluation shows that BeeQoS scales to 1K+ flows with stable performance, boosts high-/medium-priority throughput by 14.6%/36.4%, cuts median latency by 72.4%, reduces deployment overhead, and improves video QoE by 27.3% over state-of-practice baselines.

While existing far memory and disaggregated memory solutions provide a foundation for addressing limitations of single-node memory capacity and inefficient resource allocation in data centers, they predominantly focus on host memory, overlooking the critical demands of GPU-centric workloads. A key bottleneck in scaling GPU memory is the lack of connectivity and interoperability between GPUs, which is exacerbated by their heterogeneity. To bridge this gap, this paper proposes UHM, a unified data transferring and memory pooling scheme for heterogeneous GPU memories. UHM establishes the communication channels between heterogeneous GPU/host memories and leverages double data buffers for pipelined and reliable transfer. Furthermore, UHM unifies both local and remote memories to build a memory pool. The pooling scheme effectively integrates local and remote resources, performs efficient caching management in local memory, and optimizes memory block management for remote memory resources. Evaluation on a heterogeneous GPU cluster demonstrates that UHM significantly reduces the data transfer latency (up to 87.2%), improves the cache hit ratio, reduces runtime memory allocation latency (up to 94.7%), while enhancing the overall memory utilization (24.7%).

Wireless medical sensor networks (WMSNs) have been widely adopted in healthcare for collecting users' physiological data, providing crucial references for medical diagnosis and prevention. However, transmitting sensitive data over public networks faces security risks, potentially leading to privacy breaches and financial losses. Moreover, large-scale data transmission increases energy consumption, hindering continuous monitoring. Therefore, achieving energy efficiency alongside data security is critical for WMSNs. This paper proposes a lightweight slope-based piecewise linear approximation algorithm for online data compression, utilizing slope intervals under a user defined error bound, to reduce energy consumption. Concurrently, we introduce a pairing-free certificateless aggregate signature scheme, proven secure under the random oracle model against different type adversaries, to enhance data privacy and integrity. Experimental results demonstrate that the compression algorithm achieves efficient compression while preserving trends, and the aggregate signature scheme reduces computational overhead by 20% without increasing communication costs.

There is an emerging trend of using deep learning (DL) to handle complex tasks in wireless communication systems. However, recent research suggests that DL-enabled communication systems are vulnerable to adversarial attacks. Fortunately, most of these attacks are simulation-based, incapable of handling realistic channels with, e.g., multipath fading, temporal dynamics, and hardware nonlinearity, and hence lack of practicality. To this end, we present RadioShock, an over-the-air adversarial attack against wireless communication systems. Through accurate estimations of channel states for dynamic adaptations, RadioShock is made for real-world communication scenarios.We further introduce a universal compact perturbation generation algorithm, along with a new perturbation constraint strategy, aiming to achieve covert over-the-air adversarial attacks. We implement a RadioShock prototype and conduct extensive experiments using automatic modulation classification systems as a representative application scenario. The results reveal that RadioShock diminishes the accuracy of diverse models utilized in wireless communication systems by up to 52.41%, far more effective than existing simulation-based adversarial attacks in facing real-world applications.

Audio adversarial perturbations are designed to remain imperceptible to humans while deceiving automatic speech recognition (ASR) models. However, operating within the audible frequency range makes existing methods partially detectable in practice. In this paper, we present LaserAdv, a laser-based adversarial attack that injects carefully crafted perturbations via laser signals, which are superimposed on speech rather than masking it. This design exploits a well-established property of adversarial examples—the ability to mislead models through minimal, often imperceptible, modifications—while preserving the underlying speech, thereby enabling higher attack efficiency and a longer effective attack range. To mitigate distortion introduced during laser transmission, we propose SAE-TFI, a selective amplitude enhancement method in the time–frequency domain. LaserAdv enables physically realizable attacks that are inaudible and black-box, while supporting targeted and universal attack settings without requiring signal synchronization. Experimental results demonstrate that a single perturbation can cause DeepSpeech, Whisper, and iFlytek to misinterpret any of the 12,260 voice commands as target with accuracy of up to 100%, 92% and 88%, respectively. The maximum effective attack distance reaches 120 m.

With the increasing popularity of voice-centric applications, acoustic eavesdropping attacks pose a significant threat to user privacy. Although smartphones require explicit user permission to access the microphone, such attacks can bypass this restriction by exploiting power consumption data through compromised power supplies, such as USB adapters, public charging stations, and power banks. However, previous attempts can only recognize a limited set of hotwords or digits. To address this limitation, we introduce PowerEar, an acoustic eavesdropping attack that leverages the power side channel to reconstruct any audio reproduced by the built-in loudspeaker of a mobile device with an unconstrained vocabulary. Our approach relies on a combination of signal processing and generative techniques to learn the mapping between power consumption and audio playback, enabling the reconstruction of such audio through spectrogram enhancement. To validate the effectiveness of PowerEar attack, we carry out a comprehensive set of experiments using audio samples from various public personalities. Our results obtained through objective and subjective evaluations clearly demonstrate that PowerEar can successfully recover user speeches from power consumption data in comprehensive realistic settings, including speech utterances of individuals and different devices, mobile operating systems, activities, charging technology, battery and volume levels.

Biometric authentication has become increasingly popular due to its security and convenience; however, traditional biometrics are becoming less desirable in scenarios such as new mobile devices, Virtual Reality, and Smart Vehicles. For example, while face authentication is widely used, it suffers from significant privacy concerns. The collection of complete facial data makes it less desirable for privacy-sensitive applications. Lip authentication, on the other hand, has emerged as a promising biometrics method. However, existing lip-based authentication methods heavily depend on static lip shape when the mouth is closed, which can be less robust due to lip shape dynamic motion and can barely work when the user is speaking. In this paper, we revisit the nature of lip biometrics and extract shape-independent features from the lips. We study the dynamic characteristics of lip biometrics based on articulator motion. Building on the knowledge, we propose a system for shape-independent continuous authentication via lip articulator dynamics. This system enables robust, shape-independent, and continuous authentication, making it particularly suitable for scenarios with high security and privacy requirements. We conducted comprehensive experiments in different environments and attack scenarios and collected a dataset of 50 subjects. The results indicate that our system achieves an overall accuracy of 99.06% and demonstrates robustness under advanced replay attacks and AI deepfake attacks, making it a viable solution for continuous biometric authentication in various applications.

Modern vehicles are widely equipped with in-vehicle audio systems, and drivers routinely play music, podcasts, navigation prompts, phone calls, and various voice services during daily commutes. Due to the enclosed nature of the vehicle cabin and the presence of substantial ambient noise, in-vehicle content is commonly assumed to be inherently private and imperceptible from outside the vehicle. In this work, we show that this assumption does not always hold and reveal a previously unexplored leakage channel for in-vehicle audio. We propose Vehisper, the first systematic study investigating the feasibility of passively eavesdropping on in-vehicle audio from outside a moving vehicle. We discover that, during operation, in-vehicle loudspeakers excite the vehicle's metallic structure, giving rise to observable low-frequency magnetic leakage outside the vehicle, which forms a stealthy side channel that has not been explored in prior work. Building on this insight, we design a compact external sensing device and develop a multi-stage signal processing pipeline to systematically cope with the strong and complex magnetic interference introduced by vehicle motion, as well as spectral distortion and perceptual degradation induced by the leakage channel. We conduct a comprehensive evaluation of Vehisper on 20 commercial vehicles from 10 major manufacturers. Experimental results demonstrate that, under real driving conditions, Vehisper can reliably recover in-vehicle audio, achieving an average word error rate of 7.85%.

Deep learning has significantly advanced Radio Frequency (RF) sensing, leading to extensive research and practical applications in both academia and industry. However, these advancements have also introduced potential privacy and security threats to RF sensing data. In this paper, we present RFInv, the first model inversion attack targeting deep learning classifier-empowered RF sensing systems. RFInv can recover users' private sensing data without knowledge of the RF sensing model's structure, relying solely on the output prediction vector of the deep learning classifier. Consequently, this recovered sensitive data can be exploited for malicious purposes such as identity impersonation and unauthorized device control. To realize the proposed attack, we develop a deep generative adversarial network that integrates an inversion module and a critic module, enabling effective RF data recovery in black-box scenarios. To address the unique challenge of preserving physical consistency in RF data, we incorporate attention mechanisms and deformable convolutions to model their complex temporal and spatial dynamics, ensuring physical consistency. Furthermore, a spectrogram alignment loss is introduced to further enhance reconstruction accuracy. The network is trained using an auxiliary dataset, circumventing the need for access to the target model's training data. We systematically evaluate our proposed attack across multiple datasets for various RF sensing tasks and target models with different network architectures. Extensive experiments demonstrate that RFInv can recover diverse types of RF privacy data with an average Structural Similarity Index Measure (SSIM) of 0.78 and achieves an 86.21% Relative Attack Success Rate (RASR).

Palm vein recognition has gained traction in secure authentication due to its unique, stable, and inherently concealed biometric characteristics. However, the electromagnetic (EM) emissions from subcutaneous vein imaging sensors (SVIS) may unintentionally leak sensitive biometric information, fundamentally challenging the assumed security guarantees. In this paper, we propose VeinPhantom, a novel side-channel attack that reconstructs palm vein patterns from unintended EM emissions of SVIS, ultimately enabling spoofing attacks against biometric authentication systems. To overcome the challenge of low information entropy caused by weak EM signals and complex environmental interference, VeinPhantom first analyzes palm vein information from EM signals, then employs a cascaded enhancement strategy and incorporates a Dynamic Guidance Diffusion framework to progressively reconstruct high-fidelity palm vein patterns. Extensive experiments demonstrate that VeinPhantom achieves an average structure similarity index measure (SSIM) of 0.56 on commercial devices, along with a 56.96% spoofing success rate against state-of-the-art authentication systems. We further discuss potential mitigation strategies to defend against the attack.

Radio Frequency Identification (RFID) technology has emerged as a pervasive modality for human motion sensing in applications such as smart environments and healthcare monitoring. However, the inherent through-wall sensing capability of RFID technology raises critical privacy concerns regarding the unintended leakage of human motion information, a challenge that has not been adequately addressed. To fill this gap, we present a metasurface-based RFID sensing defence (MetaRFence), the first system designed to protect human motion privacy against adversarial through-wall RFID sensing. To this end, we first devise a programmable metasurface comprising 1-bit phase shifters to systematically obfuscate motion-induced signal patterns. Then, we characterize the metasurface's impact on RFID signals across temporal and spectral domains through comprehensive theoretical modeling and empirical investigations. However, our analysis reveals that it is non-trivial to achieve effective signal obfuscation in both domains, primarily due to a fundamental trade-off between increasing temporal signal variation and masking human motion in its spectrum. To overcome this, we judiciously devise a metasurface controlling strategy that jointly optimizes the signal entropy, variance, and spectrum distribution to reach a balance between temporal and spectral motion obfuscation. Our comprehensive experiments demonstrate thatMetaRFencereduces adversarial through-wall motion detection rates to$\leq$6%, decreases the F1-score of human gesture recognition to$\leq$0.11 on average, and amplifies respiration rate estimation errors by 3×, establishing a robust defense mechanism for RFID-based motion privacy protection.

The rapid proliferation of the Internet of Things (IoT) has made resource exchange and collaboration across diverse IoT domains commonplace, necessitating secure and privacy-preserving cross-domain authentication. However, existing schemes suffer from critical limitations: they lack time-bound access control, leading to persistent unauthorized access and heightened security risks, and most are incomplete, requiring resource-intensive redeployment of cryptographic mechanisms and increasing management overhead. To address these challenges, we propose a dynamic time-bound anonymous complete cross-domain authentication scheme that leverages consortium blockchain for decentralized trust, embeds dual temporal constraints, expiration time and permissible authentication periods, into credentials for fine-grained access control and automatic natural revocation, and employs accumulators and non-interactive zero-knowledge proofs (NIZKs) to enable anonymous authentication while ensuring strong privacy protection. Crucially, the proposed scheme achieves complete cross-domain authentication without modifying existing cryptographic mechanisms, significantly reducing overhead in computational, communication, and storage. Security and performance analyses confirm that the proposed scheme not only guarantees robust security and privacy but also outperforms existing schemes in efficiency.

The widespread adoption of data-driven services, including networked data collection and analysis systems, has greatly enhanced convenience and decision-making, but it has also raised growing concerns about the trade-off between fine-grained utility and personalized privacy guarantees. Personalized Differential Privacy (PDP) offers a flexible framework by allowing users to specify individualized privacy budgets. However, existing sampling-based PDP mechanisms often rely on coarse risk modeling assumptions that treat individual data characteristics uniformly, leading to suboptimal utility and inefficient privacy expenditure. In this paper, we propose the Utility-Aware Sampling Mechanism (UASM), a principled PDP implementation that enables fine-grained, user-centric privacy control while explicitly optimizing utility. First, UASM formalizes policy-assisted secret specifications, allowing confidentiality to be determined through a combination of baseline protection rules and personalized privacy preferences, and combines them with individualized privacy budgets. Second, UASM employs a two-stage utility-aware sampling strategy to calibrate noise: (i) an optimal global threshold selected to reduce unnecessary privacy-budget wastage while respecting users’ declared budgets, and (ii) a sensitivity-aware refinement stage that allocates privacy loss according to each record’s influence on query accuracy. Formal privacy analysis demonstrates that UASM provides rigorous privacy guarantees and promotes fairer privacy expenditure under heterogeneous privacy requirements. Extensive experiments on synthetic and real-world datasets, including network-oriented downstream tasks, show that UASM achieves a superior privacy-utility trade-off over state-of-the-art PDP baselines, underscoring its practical effectiveness.

In the era of pervasive online social networks (OSNs), the erosion of information privacy is occurring at an unprecedented rate. Empowering individuals with user-centric control over their private information is crucial to fostering public confidence in OSN services. Hence, the investigation into the personalized privacy configurations within the framework of differential privacy for OSNs, particularly for social relationships, is captivating. In this paper, we introduce a Collaborative Personalized Edge Differential Privacy model (CPEDP), ensuring personalized protection for sensitive social relationships while retaining the high utility of network features. Specifically, CPEDP allows each user to define a policy specification consisting of two complementary components: secret specifications at the edge level to identify sensitive relationships, and privacy specifications at the user level to determine personalized privacy parameters. These user-defined preferences are integrated through a collaborative privacy decision-making process that ensures consistent and interpretable privacy guarantees. Furthermore, we formalize the privacy primitive of CPEDP and develop a sampling-based mechanism to effectively implement the proposed model. Finally, comparative experiments on real-world datasets confirm that CPEDP achieves superior privacy-utility trade-offs, yielding more accurate estimates of key graph statistics through policy-driven personalization.

With the advancement of technology, the functionality of mobile applications has become increasingly powerful, and users are heavily relying on these apps for various entertainment and business activities. However, the frequent use of these applications accelerates smartphone battery consumption, which can require multiple charging sessions per day. As a result, USB charging facilities and shared mobile power banks have become more widespread. Although their popularity indeed provides convenience to users, it also introduces security risks, such as information theft through USB data transfer. In this paper, we propose PowerApp, a novel framework that identifies mobile applications running and ongoing user actions by analyzing their energy consumption of a USB-connected mobile device. In particular, we passively measure the current withdrawal by the charging mobile device from the power source. We demonstrate the effectiveness and practicality of PowerApp through extensive experiments involving 190 popular mobile apps. Our experimental results show that PowerApp can successfully identify these apps with around 95% accuracy on average.

Mobile Edge Computing (MEC) utilizes edge servers to offload the computational burden from cloud infrastructure. By providing low-latency and high-bandwidth services, MEC enables mobile users and IoT devices to efficiently offload and execute computational tasks at the network edge. However, optimizing communication and computational resources in a multi-user, multi-server MEC environment remains a significant challenge. In this paper, we propose TSAJS, an efficient multi-server joint task scheduling scheme designed to enhance the effectiveness of MEC offloading. We model the task offloading and resource allocation problem as a Mixed-Integer Nonlinear Programming (MINLP) problem, aiming to maximize user offloading gain by minimizing task completion time and energy consumption. A heuristic algorithm for offloading is introduced by combining threshold-triggering and simulated annealing to effectively avoid local optima and converge toward the global optimum. Meanwhile, the optimal solution for resource allocation is derived using the Karush-Kuhn-Tucker (KKT) conditions. Experimental results demonstrate that TSAJS delivers near-optimal performance, outperforming traditional methods in terms of user offloading effectiveness. Its efficiency enables solution finding within polynomial time, while also adapting to the preferences of users and service providers.

Audio recognition systems have become integral to various applications, including speech-to-text, virtual assistants, and security monitoring, where efficiency and privacy are key concerns. The collaborative recognition system (CRS) partitions and deploys the neural network (NN) across multiple edge devices for cooperative recognition without sharing raw audio data. This distributed approach significantly alleviates the computational burden on the client and ensures data privacy. These advantages make CRS increasingly prevalent in audio recognition applications to improve security, efficiency, and provide timely feedback. However, sharing information during collaboration still poses the risk of exposing original data. To the best of our knowledge, this paper introduces InverCRS, the first inversion attack targeting CRS-empowered audio recognition systems. InverCRS is a generative attack in which the attacker trains a local generative model to take intermediate results as input and output the original audio. Once the generative model is trained, it can perform audio inversion using new intermediate results without the need for further optimization. Furthermore, InverCRS utilizes heuristic algorithms to approximate gradients, making it applicable to both white-box and black-box scenarios. We conduct comprehensive experiments to evaluate the feasibility and efficiency of InverCRS across two real-world audio datasets. The results demonstrate that InverCRS can effectively reconstruct the original audio from various split points within the CRS. Additionally, we investigate two potential defense strategies and provide experimental evaluations of their effectiveness in mitigating this attack.

As network scale and complexity continue to increase, managing network configurations has become an increasingly challenging task. Existing configuration tools often depend on low-level, abstract intermediate representations, which require users to have substantial technical expertise. This reliance not only increases the learning curve but also heightens the risk of configuration errors. Recent advances in Large Language Models (LLMs) have demonstrated strong potential for automating tasks across various domains. However, their applications to network configuration generation remain limited due to several challenges, including hallucination, restricted context length, and insufficient adaptability to domain-specific requirements. To address these issues, we propose ConfAgent, an advanced network configuration generation system powered by a multi-model intelligent agent. ConfAgent comprises four key components: a conflict detector, an information extractor, a routing algorithm coder, and a formal synthesizer. These components collaborate to accurately interpret complex configuration intents, detect potential conflicts, and generate robust code and network configurations through intuitive natural language interactions. Extensive experiments conducted on the NetConfEval benchmark demonstrate that ConfAgent consistently outperforms existing state-of-the-art methods by margins ranging from 36 % to 100 %, particularly excelling in configuration tasks for large-scale network topologies.

Network traffic classification plays a crucial role in a wide range of applications, e.g., Quality of Service (QoS) enhancement, resource management, and network security. However, the widespread adoption of encryption protocols (e.g., SSL/TLS) and the emergence of anonymous communication systems (e.g., Tor) have introduced significant challenges due to the presence of complex and varied network noise. Although considerable effort has been made to improve the robustness of the traffic classification, the performances of existing state-of-theart methods cannot be guaranteed in the presence of a mixture of noises, and are not stable in different application scenarios. In this paper, we innovate in proposing a network traffic classification method based on MoA (Mixture of Agents), namely MOTA. By leveraging a light-weight MoA architecture, MOTA efficiently fine-tunes mainstream Large Language Models (LLMs) to adapt to different application scenarios of traffic classification, and fully exploits the collaboration of the LLMs to ensure the robustness against mixed noises. Our extensive experiments show that, the classification accuracy is$\geq 99 {\%}$across multiple public datasets injected with mixed noises, significantly outperforming existing SOTA methods. Moreover, despite incorporating multiple LLMs, MOTA maintains millisecond-level inference latency on a server equipped with four NVIDIA GeForce RTX 4090 GPUs, owing to its lightweight design.

With the development of the Android system and increasing screen size, the use of multi-window mode has become prevalent among users. However, the security and privacy implications associated with this mode have not been thoroughly investigated. This paper uncovers severe and unique security vulnerabilities in Android's multi-window mode, revealing several high-risk side-channels that facilitate diverse cross-window attacks, leading to significant breaches of user privacy. In detail, our research introduces WinSpy, a framework leveraging a newly discovered resource contention side-channel in multi-window mode to fingerprint app launches, web pages, and in-app activities, all without violating Android's permission framework. Our extensive evaluations demonstrate that WinSpy achieves high accuracy (from 70 to 80% detecting website and app launches to over 97% recognizing critical in-app activities). Additionally, we reveal that due to Android's lenient permission management for this mode, window apps can also use Inertial Measurement Unit sensors to launch attacks, such as inferring the user's touch positions outside the window with high precision. Furthermore, we propose systematic mitigations against these vulnerabilities.

The recent proliferation of spacecraft in Earth's orbits has ushered in the rise of large-scale satellite constellations. However, this unprecedented growth of constellations has introduced a previously unforeseen challenge: ground track congestion. Specifically, the increasing density of orbital slots forces satellites to share similar orbit planes, causing their nadir-point projections on Earth's surface (i.e., ground tracks) to overlap or remain in close proximity within short time intervals. Such orbit-endowed ground track congestion can degrade constellation performance in remote sensing operations, specified by limited constellation coverage, redundant satellite count, and delayed data delivery.

Cloud-fog automation architecture has propelled the advancement of the Industrial Internet of Things (IIoT), significantly enhancing production efficiency and intelligence through extensive data collection and connectivity. Simultaneously, industrial cyber-physical system leverages this data to achieve intelligent control and optimization of production processes. As industrial production becomes increasingly specialized and complex, independent operations within a single domain are no longer sufficient to meet demands, making cross-domain collaborative production inevitable. Consequently, ensuring the security of cross-domain communication and data sharing has become a critical issue for IIoT under the cloud-fog automation architecture. Existing solutions encounter substantial management and computational burdens in cross-domain communication and data sharing, and they are vulnerable to privacy leakage risks. To address these challenges and enhance industrial production efficiency, this paper uses consortium blockchain to co-design a cross-domain authentication and data sharing scheme. The scheme ensures secure and private cross-domain communications with minimal computational, communication, and storage overhead. And, the proposed time-specific plaintext checkable encryption protocol can secure data during cross-domain sharing. Security and performance analyses show that the proposed scheme effectively reduces computational and communication resource demands while maintaining communication and data security.

The pursuit of refined data analysis and the preservation of privacy in Big Data pose significant concerns. Among the paramount paradigms for addressing these challenges, differential privacy stands out as a vital area of research. However, traditional differential privacy tends to be excessively restrictive when it comes to individuals’ control over their own data. It often treats all data as inherently sensitive, whereas in reality, not all information related to individuals is sensitive and requires an identical level of protection. In this paper, we define secret specification-based differential privacy (SSDP), where the term “secret specification” implies enabling users to decide what aspects of their information are sensitive and what are not, prior to data generation or processing. By allowing individuals to independently define their secret specifications, the SSDP achieves personalized privacy protection and facilitates effective data analysis. To enable the targeted application of SSDP, we further present task-specific mechanisms designed for database and graph data scenarios. Finally, we assess the trade-offs between privacy and utility inherent in the proposed mechanisms through comparative experiments conducted on real datasets, demonstrating the utility enhancements offered by SSDP mechanisms in practical applications.

The proliferation of Internet of Things (IoT) devices has led to a surge in data generation within smart home environments. This data explosion has raised significant privacy concerns and highlighted a lack of user-friendly controls. Consequently, there is a pressing need for a robust privacy-enhancing mechanism tailored for smart homes, safeguarding sensitive data from a user-centric perspective. In this paper, we introduce the Fog-enhanced Personalized Differential Privacy (FEPDP) model, which utilizes the distributed nature of fog computing to improve data processing efficiency and security in smart homes. Specifically, the personalization, as a key feature of FEPDP, is manifested through an array of user-driven policy specifications, enabling home users to specify secret and privacy specifications for their personal data. These specifications not only enhance control over personal data but also align with the heterogeneous nature of smart home environments. Subsequently, aligned with fog-based smart home architecture, we propose two policy-driven partitioning mechanisms that utilize threshold partitioning based on dynamic programming to effectively implement FEPDP. Finally, comprehensive theoretical analysis and experimental validation across various statistical analysis tasks and datasets confirm that FEPDP achieves a superior privacy-utility trade-off for smart home data by leveraging non-sensitive data and fog-based partitioning.

With the prevalence of online social networks (OSNs), much personal information is collected and maintained by trusted service providers for third-party queries and analyses. Existing works regarding differentially private social network data publication overlook the fact that different users exhibit distinct privacy preferences or sensitivity inclinations. Neglecting these individual nuances may lead to privacy mechanisms that are overly conservative or inadequately protective. Furthermore, the injection of excessive noise into OSN data perceived by users as non-personal or less sensitive can incur additional privacy costs, resulting in lower service quality. This paper introduces a fine-grained, sensitivity-aware personalized edge differential privacy model (SPEDP) for OSNs. Specifically, SPEDP enables each OSN user to individually define the sensitivity level of their social connections, facilitating user-friendly personalized privacy settings. We design a privacy-aware mechanism that operates within a trusted service provider, capable of establishing privacy protection levels based on user-perceived sensitivity settings. Additionally, we propose a sensitivity-aware sampling mechanism to implement SPEDP. To further optimize the privacy mechanism, we explore a privacy threshold optimization strategy aimed at minimizing privacy budget waste. Finally, the personalized privacy protections and utility improvements achieved by the SPEDP mechanism are rigorously validated through theoretical analysis and comprehensive comparative experiments on benchmark datasets.

Is it secure to measure the reliability of local models by similarity in federated learning (FL)? This paper delves into an unexplored security threat concerning applying similarity metrics, such as the$L_{2}$norm, Euclidean distance, and cosine similarity, in protecting FL. We first uncover the deficiencies of similarity metrics that high-dimensional local models, including benign and poisoned models, may be evaluated to have the same similarity while being significantly different in the parameter values. We then leverage this finding to devise a novel untargeted model poisoning attack, Faker, which launches the attack by simultaneously maximizing the evaluated similarity of the poisoned local model and the difference in the parameter values. Experimental results based on seven datasets and eight defenses show that Faker outperforms the state-of-the-art benchmark attacks by1.1-9.0Xin reducing accuracy and1.2-8.0Xin saving time cost, which even holds for the case of a single malicious client with limited knowledge about the FL system. Moreover, Faker can degrade the performance of the global model by attacking only once. We also preliminarily explore extending Faker to other attacks, such as backdoor attacks and Sybil attacks. Lastly, we provide a model evaluation strategy, called the similarity of partial parameters (SPP), to defend against Faker. Given that numerous mechanisms in FL utilize similarity metrics to assess local models, this work suggests that we should be vigilant regarding the potential risks of using these metrics. The code will be released soon.

Given the wide adoption of multimodal sensors (e.g., camera, lidar, radar) byautonomous vehicles (AVs), deep analytics to fuse their outputs for a robust perception become imperative. However, existing fusion methods often make two assumptions rarely holding in practice: i) similar data distributions for all inputs and ii) constant availability for all sensors. Because, for example, lidars have various resolutions and failures of radars may occur, such variability often results in significant performance degradation in fusion. To this end, we present t-READi, an adaptive inference system that accommodates the variability of multimodal sensory data and thus enables robust and efficient perception. t-READi identifies variation-sensitive yetstructure-specificmodel parameters; it then adapts only these parameters while keeping the rest intact. t-READi also leverages a cross-modality contrastive learning method to compensate for the loss from missing modalities. Both functions are implemented to maintain compatibility with existing multimodal deep fusion methods. The extensive experiments evidently demonstrate that compared with the status quo approaches, t-READi not only improves the average inference accuracy by more than 6% but also reduces the inference latency by almost 15× with the cost of only 5% extra memory overhead in the worst case under realistic data and modal variations.

Acoustic eavesdropping against private or confidential spaces is a significant threat in the realm of privacy protection. While the presence of soundproof material would weaken such an attack, current eavesdropping technology may be able to bypass these protections. Fortunately, existing studies either inadequately cover the full spectrum of human speech due to low-frequency responses or rely heavily on the prior knowledge used to train a model. To address these challenges, this paper introduces mmEcho, a new acoustic eavesdropping method that utilizes millimeter-wave signals to sense vibration induced by sound precisely. Through signal processing techniques such as the intra-chirp scheme and phase calibration algorithm, mmEcho achieves micrometer-level vibration extraction without requiring target-related data. To improve the range of eavesdropping attacks while reducing noise, we optimize radar signals by leveraging the widespread availability of multiple antennas on commercial off-the-shelf radars. We comprehensively evaluate the performance of mmEcho in different real-world settings. Experimental results demonstrate that, with the aid of multi-antenna technology, mmEcho can more effectively reconstruct the audio from the target at various distances, directions, sound insulators, reverberating objects, sound levels, and languages. Compared to existing methods, our approach provides better effectiveness without prior knowledge, such as the speech data from the target.

Recent studies have demonstrated the feasibility of eavesdropping on audio via radio frequency signals or videos, which capture physical surface vibrations from surrounding objects. However, these methods are inadequate for intercepting internally transmitted audio through wired media. In this work, we introduce radio-frequency retroreflector attack (RFRA) and bridge this gap by proposing an RFRA-based eavesdropping system,RF-Parrot${}^{\mathbf {2}}$, capable of wirelessly capturing audio signals transmitted through earphone wires. Our system entails embedding a tiny field-effect transistor within the wire to establish a battery-free retroreflector, whose reflective efficiency is correlated with the amplitude of the audio signal. To preserve the details of audio signals, we designed a unique retroreflector using a depletion-mode MOSFET (D-MOSFET). This MOSFET can be triggered by any voltage level present in the audio signals, thus guaranteeing no information loss during activation. However, the D-MOSFET introduces a nonlinear convolution operation on the original audio, resulting in distorted audio eavesdropping. Thus, we devised an engineering solution which utilized a novel convolutional neural network in conjunction with an efficient Parallel WaveGAN vocoder to reconstruct the original audio. Our comprehensive experiments demonstrate a strong similarity between the reconstructed audio and the original, achieving an impressive 95% accuracy in speech command recognition.

Object identification is a pivotal enabling technique for smart home and manufacturing applications. Traditional methodologies for object identification predominantly rely on a singular sensor modality, which inherently limits their ability to furnish a detailed characterization of the target object. Addressing this deficiency, in this paper, we fill this gap by introducing CRFUSION, the first-of-its-kind system that integrates the object RGB image and the radio frequency (RF) signal reflected by the object for fine-grained object identification. CRFUSION leverages the complementary characteristics between visible light and radio frequency modalities to simultaneously determine the category and material of target objects. We design a multifaceted object feature from the RF signal, called the Energy Reflection Factor (ERF), which not only reveals the object texture but complements the image modality for identifying the object category. By integrating the characteristics of radar, we obtain radar feature maps based on the ERF of target objects. Additionally, we have developed a modality fusion network to comprehensively integrate the image and ERF features. We conducted a comprehensive evaluation of CRFUSION using a commercial mmWave radar development board and camera. The results show that CRFUSION achieves a classification accuracy of over 96%, demonstrating its robustness, and potential for application.

Real-time velocity monitoring is pivotal for fault detection of rotating machinery. However, existing methods rely on either troublesome deployments of optical encoders and IMU sensors or various tachometers delivering coarse-grained velocity measurements insufficient for fault detection. To overcome these limitations, we proposeRomeoas the first work to exploit the mmWave radar forrotatingmachinery fault detection by extracting a fine-grained velocity signature. Though mmWave radars should capture instant rotation information with their claimed high sensitivity and sampling rate, direct adoption entails significant efforts for high-precision velocity measurement per radar to handle; particularly, exhausted system calibration and noise interference. To this end, we first develop a phase-velocity model to characterize the relationship between the mmWave signal phase and the fine-grained angular velocity. We then explore the geometric properties of specific positions in the rotation trajectory to precisely calibrate the rotation sensing model, leading to an iterative algorithm for accurate angular velocity measurement. Finally, we propose a simple yet effective fault detection algorithm by extracting a unique velocity signature. Our extensive experiments showRomeoachieves a median error of 0.4$^\circ$/s for fine-grained angular speed measurement, outperforming SOTA solutions with over ×16 angular speed granularity and ×7 measurement precision.

Sound eavesdropping using light has been an area of considerable interest and concern, as it can be achieved over long distances. However, previous work has often lacked stealth (e.g., active emission of laser beams) or been limited in the range of realistic applications (e.g., using direct light from a device’s indicator LED or a hanging light bulb). In this paper, we presentEchoLight, a non-intrusive, passive and long-range sound eavesdropping method that utilizes the extensive reflection of ambient light from vibrating objects to reconstruct sound. We analyze the relationship between reflection light signals and sound signals, particularly in situations where the frequency response of reflective objects and the efficiency of diffuse reflection are suboptimal. Based on this analysis, we have introduced an algorithm based on cGAN to address the issues of nonlinear distortion and spectral absence in the frequency domain of sound. We extensively evaluateEchoLight’s performance in a variety of real-world scenarios. It demonstrates the ability to accurately reconstruct audio from a variety of source distances, attack distances, sound levels, light sources, and reflective materials. Our results reveal that the reconstructed audio exhibits a high degree of similarity to the original audio over 40 meters of attack distance.

Neural network models have become integral to Internet of Things (IoT) systems, with applications spanning from industrial automation to critical infrastructure management. Despite their prevalence, the deployment of these models within IoT systems introduces distinctive security vulnerabilities. In particular, adversaries may execute model poisoning attacks, which aim to alter the decision-making processes of embedded models, leading to erroneous outcomes. Existing model poisoning attacks necessitate access to extensive auxiliary datasets, such as the training dataset itself or one with same distribution. These requirements often render such attacks impractical in IoT contexts, given the constrained storage and computational resources of IoT devices. This paper proposes the first model poisoning attack against interpreters without auxiliary datasets to manipulate the model’s behavior. We evaluate the attack on three real-world datasets, and results indicate that this attack can successfully coerce the targeted interpreters to produce outcomes aligned with an adversary’s intentions, while maintaining nearly indistinguishable performance from the original model, thereby ensuring its stealthiness. Furthermore, beyond directly affected interpreters, our experiments reveal that four additional interpreters coupled to the poisoned model are indirectly influenced, underscoring the attack’s transferability.

Attacks on speech recognition systems often use adversarial or inaudible commands. However, a challenge is that adversarial perturbations typically fall within the audible frequency range, making it difficult to achieve inaudibility. Additionally, the non-linear effects of loudspeakers often cause inaudible commands to become audible at higher power levels. Therefore, minimizing the power requirements of the attack is essential to maintain inaudibility. Another significant obstacle is the conversion of variable-length commands, especially longer ones, into shorter target commands. In this paper, we present UltraAdv, a method for generating long-range adversarial perturbations capable of compromising commands of arbitrary length in closed-box setting. By combining the ultrasonic signal with the normal one, rather than negating it as in DolphinAttack, we significantly improve the energy efficiency, thus enhancing its attack distance. We also propose a dynamically adjustable suppression-interference method based on automatic gain control to address the challenge of mismatched durations between long commands and target commands (length-independent). Experiments demonstrate that using a single perturbation, we achieve impressive success rates of 98.84% and 96.62% and 98.32% across a diverse set of 12,260 speeches on DeepSpeech, iFlytek, and Whisper. The attack range reaches up to 15 m, surpassing DolphinAttack's 5 m range at equivalent power.

Internet of Things (IoT) devices are frequently deployed in highly dynamic environments and need to continuously learn new classes from data streams. Incremental Learning (IL) has gained popularity in IoT as it enables devices to learn new classes efficiently without retraining model entirely. IL involves fine-tuning the model using two sources of data: a small amount of representative samples from the original training dataset and samples from the new classes. However, both data sources are vulnerable to Membership Inference Attack (MIA). Fortunately, the existing MIAs result in poor performance against IL, because they ignore features such as the similarity between old and new models at the old classification layer. This paper presents the first MIA against IL, capable of determining not only whether a sample was used for training/fine-tuning but also distinguishing whether it belongs to the representative dataset or the new classes (unique in IL). Extensive experiments validate the effectiveness of our attack across four real-world datasets. Our attack achieves an average attack success rate of 74.03% in the white-box setting (model structure and parameters are known) and 70.08% in the black-box setting. Importantly, our attack is not sensitive to the IL hyper-parameters (e.g., distillation temperature), confirming its accurate, robust, and practical.

With the increasing popularity of virtual reality applications, there is an increasing demand for more interactive entertainment, learning, social interactions, and other activities on eyewear devices. Recognizing users’ emotion and providing reliable feedback can significantly improve the immersive experience for users. However, previous works in emotion recognition required modifications to existing eyewear devices and the integration of additional sensors, or relied on specialized sensors in expensive commercial-grade eyewear devices, making direct deployment on existing consumer-grade eyewear devices challenging. In this paper, we proposeAccEmo, the first system that analyzes the data from the built-in accelerometer sensor on eyewear devices to accurately recognize human emotion.AccEmofirst employs signal processing technologies to process raw accelerometer data, and then uses a binary classification network to determine whether the accelerometer data is influenced by emotional changes. Subsequently,AccEmoproposes a network architecture based on residual neural network and channel-wise attention mechanism as a universal feature extractor to extract complex features related to human emotions from the accelerometer data. Finally,AccEmouses personalized classifiers to achieve emotion recognition for different users. Extensive performance evaluation ofAccEmoacross diverse users demonstrates an exceptional average accuracy of 94.3%. Additionally, the robustness ofAccEmois validated through evaluations in various scenarios, yielding promising results.

Recent works demonstrated that we can eavesdrop on audio by using radio frequency signals or videos to capture the physical surface vibrations of surrounding objects. They fall short when it comes to intercepting internally transmitted audio through wires. In this work, we first address this gap by proposing a new eavesdropping system, RF-Parrot, that can wirelessly capture the audio signal transmitted in earphone wires. Our system involves embedding a tiny field-effect transistor in the wire to create a battery-free retroreflector, with its reflective efficiency tied to the audio signal’s amplitude. To capture full details of the analog audio signals, we engineered a novel retroreflector using a depletion-mode MOSFET, which can be activated by any voltage of the audio signals, ensuring no information loss. We also developed a theoretical model to demystify the nonlinear transmission of the retroreflector, identifying it as a convolution operation on the audio spectrum. Subsequently, we have designed a novel convolutional neural network-based model to accurately reconstruct the original audio. Our extensive experimental results demonstrate that the reconstructed audio bears a strong resemblance to the original audio, achieving an impressive 95% accuracy in speech command recognition.

Sound eavesdropping using light has been an area of considerable interest and concern, as it can be achieved over long distances. However, previous work has often lacked stealth (e.g., active emission of laser beams) or been limited in the range of realistic applications (e.g., using direct light from a device’s indicator LED or a hanging light bulb). In this paper, we present EchoLight, a non-intrusive, passive and long-range sound eavesdropping method that utilizes the extensive reflection of ambient light from vibrating objects to reconstruct sound. We analyze the relationship between reflection light signals and sound signals, particularly in situations where the frequency response of reflective objects and the efficiency of diffuse reflection are suboptimal. Based on this analysis, we have introduced an algorithm based on cGAN to address the issues of nonlinear distortion and spectral absence in the frequency domain of sound. We extensively evaluate EchoLight’s performance in a variety of real-world scenarios. It demonstrates the ability to accurately reconstruct audio from a variety of source distances, attack distances, sound levels, light intensity, light sources, and reflective materials. Our results reveal that the reconstructed audio exhibits a high degree of similarity to the original audio over 40 meters of attack distance.

In today’s information age, eavesdropping has been one of the most serious privacy threats in information security, such as exodus spyware (Rudie et al., 2021) and pegasus spyware (Anatolyevich, 2020). And the main one of them is acoustic eavesdropping. Acoustic eavesdropping (George and Sagayarajan, 2023) is a technology that uses microphones, sensors, or other devices to collect and process sound signals and convert them into readable information. Although much research has been done in this area, there is still a lack of comprehensive investigation into the timeliness of this technology, given the continuous advancement of technology and the rapid development of eavesdropping methods. In this article, we have given a selective overview of acoustic eavesdropping, focusing on the methods of acoustic eavesdropping. More specifically, we divide acoustic eavesdropping into three categories: motion sensor-based acoustic eavesdropping, optical sensor-based acoustic eavesdropping, and RF-based acoustic eavesdropping. Within these three representative frameworks, we review the results of acoustic eavesdropping according to the type of equipment they use and the physical principles of each. Secondly, we also introduce several important but challenging applications of these acoustic eavesdropping methods. In addition, we compared the systems that meet the requirements of acoustic eavesdropping in real-world scenarios from multiple perspectives, including whether they are non-intrusive, whether they can achieve unconstrained word eavesdropping, and whether they use machine learning, etc. The general template of our article is as follows: firstly, we systematically review and classify the existing eavesdropping technologies, elaborate on their working mechanisms, and give corresponding formulas. Then, these eavesdropping methods were compared and analyzed, and each method’s effectiveness and technical difficulty were evaluated from multiple dimensions. In addition to an assessment of the current state of the field, we discuss the current shortcomings and challenges and give a fruitful direction for the future of acoustic eavesdropping research. We hope to continue to inspire researchers in this direction.

With the prevalence of various sensors and smart devices in people’s daily lives, numerous types of information are being sensed. While using such information provides critical and convenient services, we are gradually exposing every piece of our behavior and activities. Researchers are aware of the privacy risks and have been working on preserving privacy while sensing human activities. This survey reviews existing studies on privacy-preserving human activity sensing. We first introduce the sensors and captured private information related to human activities. We then propose a taxonomy to structure the methods for preserving private information from two aspects: individual and collaborative activity sensing. For each of the two aspects, the methods are classified into three levels: signal, algorithm, and system. Finally, we discuss the open challenges and provide future directions.

The rapid evolution of the smart grid has made the security and reliability of communication within the power system an urgent and critically important issue. To address this challenge, authentication and key agreement (AKA) protocols have gained significant attention and are regarded as indispensable tools for ensuring the secure operation of the smart grid. However, traditional AKA protocols are plagued by a series of issues, including cumbersome certificate management, delayed certificate revocation, and vulnerability to man-in-the-middle attacks. With the emergence of certificate-less public key cryptography (CL-PKC), the integration of conventional AKA protocols with CL-PKC has emerged as a prominent trend. This paper presents an enhanced certificate-less AKA protocol for smart grids, named ECL-AKA. Firstly, the paper outlines the architecture and security model of this protocol. Subsequently, it presents the complete workflow of the ECL-AKA protocol. Notably, the ECL-AKA protocol introduces a private key verification step before key agreement, allowing for rapid screening of malicious requests at a lower computational cost, thereby enhancing the protocol’s resistance to various types of attacks. In addition, the ECL-AKA’s security is formally established through rigorous theoretical proofs based on the random oracle model in the paper. Finally, comparative experimental analysis demonstrates that the ECL-AKA exhibits lower computational and communication overhead while satisfying essential security attributes.

Currently, power data trading typically relies on Web pages as the conventional mode of mediation. Nevertheless, dishonest trading Web may secretly resell the data sets of grid companies or have no way of knowing what the buyer has done with the power data, thereby compromising the privacy of power user. This article proposes a privacy-preserving supply-demand consistency matching service scheme (PPMSE) to address the problem of whether the power data provided by the seller aligns with the requirements of the buyer in power data trading. The scheme utilizes enhanced public-key searchable encryption (PKSE) to establish a matching environment that fulfills privacy protection needs, thereby facilitating consistency matching between supply and demand, all while preserving user privacy. Then, the PPMSE ensures that matching service can only occur within the designated platform by equipping the power data trading cloud platform with public and private keys. Additionally, by applying ciphertext policy attribute-based encryption (CP-ABE) to the data processing tasks of the buyer, the scheme enables the seller to decrypt and obtain what the buyer has done with the data after successful matching and meeting specific attributes. Ultimately, a comprehensive analysis and performance evaluation are provided, validating the feasibility and superiority of the proposed scheme.

The surge in popularity of wireless headphones, particularly wireless earbuds, as smart wearables, has been notable in recent years. These devices, empowered by artificial intelligence (AI), are broadening their utility in areas such as speech recognition, augmented reality, pose recognition, and health care monitoring, thereby enriching user experiences through novel interactive interfaces driven by embedded sensors. However, the widespread adoption of wireless earbuds has spurred concerns regarding security and privacy, necessitating robust bespoke security measures. Despite the miniaturization of mobile chips enabling the integration of sophisticated algorithms into smart wearables, the research and industrial communities have yet to accord adequate attention to earbud security. This paper focuses on empowering wireless earbuds to authenticate their legitimate users, tackling the challenges associated with conventional authentication methods. Instead of relying on input interface authentication methods like PIN or lock patterns, this research delves into leveraging Inertial Measurement Unit (IMU) data collected during interactions with devices to extract novel biometric features, presenting an alternative approach that nonetheless confronts challenges related to signal capture and interference. Consequently, we propose and design BudsAuth, an implicit user authentication framework that harnesses built-in IMU sensors in smart earbuds to capture vibration signals induced by on-face touching interactions with the earbuds. These vibrations are utilized to deliver continuous and implicit user authentication with high precision and compatibility across various earbud models. Extensive evaluation demonstrates BudsAuth’s capability to achieve an Equal Error Rate (EER) of 0.0003, representing an approximate 99.97% accuracy with seven consecutive samples of interactive gestures for implicit authentication.

With the development of big data technology, the power industry has also entered the data-driven intelligence era. Cloud computing-based smart grids give the power industry stronger capabilities in data analytics. Electricity load forecasting in the cloud helps smart grids allocate resources appropriately. However, the users' privacy is easily compromised in the load forecasting process with cloud computing. The electricity usage data collected by the system may contain sensitive information about the users, which could lead to serious privacy leakage. In order to solve the issues, we propose a novel privacy-preserving cloud-aided load forecasting scheme for the cloud computing-based smart grid. It contains a secure online training algorithm and an efficient real-time forecasting algorithm. Meanwhile, the two-party interaction security scheme is more suitable for real-world applications. Before being sent to the cloud server, the control center of the smart grids encrypts the data using homomorphic encryption. During the process of model training and forecasting, the data remains securely encrypted at all times to avoid the risk of data privacy breaches. Finally, security and experimental analyses show that our scheme effectively avoids privacy leakage while reducing resource consumption.

Data aggregation plays a crucial role in smart grid communication as it enables the collection of data in an energy-efficient manner. However, the widespread deployment of smart meters has raised significant concerns regarding the privacy of users' personal data. Therefore, in this paper, we present an efficient and privacy-preserving data aggregation and trust management scheme (PATM) for an IoT-enabled smart grid based on smart contract. Firstly, we propose a five-layer architecture for smart grid communication to support secure and efficient data aggregation and management. Under the architecture, the Boneh-Goh-Nissim cryptosystem with blind factor is improved to facilitate privacy protection. In addition, the tamper-evident nature of blockchain is utilized for effective data management. Our designs also enhance the resistance to differential attack and prevent privacy breaches during the aggregation process. Detailed security proof and theoretical analysis confirm that our PATM can satisfies the necessary security and privacy requirements while maintaining the required efficiency for smart grid operations. Furthermore, comparative experiments demonstrate that PATM outperforms other proposed work in terms of storage cost, computational complexity, and utility of differential privacy.

Most IoT systems involve IoT devices, communication protocols, remote cloud, IoT applications, mobile apps, and the physical environment. However, existing IoT security analyses only focus on a subset of all the essential components, such as device firmware or communication protocols, and ignore IoT systems' interactive nature, resulting in limited attack detection capabilities. In this work, we proposeIota, a logic programming-based framework to perform system-level security analysis for IoT systems.Iotagenerates attack graphs for IoT systems, showing all of the system resources that can be compromised and enumerating potential attack traces. In buildingIota, we design novel techniques to scan IoT systems for individual vulnerabilities and further create generic exploit models for IoT vulnerabilities. We also identify and model physical dependencies between different devices as they are unique to IoT systems and are employed by adversaries to launch complicated attacks. In addition, we utilize NLP techniques to extract IoT app semantics based on app descriptions.Iotaautomatically translates vulnerabilities, exploits, and device dependencies to Prolog clauses and invokes MulVAL to construct attack graphs. To evaluate vulnerabilities' system-wide impact, we propose three metrics based on the attack graph, which provide guidance on hardening IoT systems. Evaluation on 127 IoT CVEs (Common Vulnerabilities and Exposures) shows thatIota's exploit modeling module achieves over 80% accuracy in predicting vulnerabilities' preconditions and effects. We applyIotato 37 synthetic smart home IoT systems based on real-world IoT apps and devices. Experimental results show that our framework is effective and highly efficient. Among 27 shortest attack traces revealed by the attack graphs, 62.8% are not anticipated by the system administrator. It only takes 1.2 seconds to generate and analyze the attack graph for an IoT system consisting of 50 devices.

Video surveillance systems play a crucial role in ensuring public safety and security by capturing and monitoring critical events in various areas. However, traditional surveillance cameras face limitations when it comes to malicious physical damage or obscuring by offenders. To overcome this limitation, we proposem$^{2}$2Vision, which is the first millimeter-wave (mmWave)-based video reconstruction system designed to enhance existing video surveillance cameras.m$^{2}$2Visionutilizes mmWave to sense the profile and motion signature of the target, integrating it with previously acquired visual data about the environment and the target's appearance, thereby facilitating the reconstruction of surveillance video. Specifically, our proposed system incorporates a dual-stage mmWave signal denoising algorithm to efficiently eliminate the noise and multiple-input multiple-output virtual antenna enhanced heatmap generation (MVAE-HG) method to obtain fine-grained mmWave heatmaps responsive to the target's profile and motion information. Moreover, we design the mm2Video generative network that first employs a multi-modal fusion module to fuse the mmWave and pre-acquired visual data, then use a conditional generative adversarial network (cGAN)-based video reconstruction module for surveillance video reconstruction. We conducted comprehensive experiments onm$^{2}$2Visionusing a commercial mmWave radar and four surveillance cameras across various environments, with the participation of seven individuals. Evaluation results show thatm$^{2}$2Visioncan achieve an average structural similarity index measure (SSIM) of 0.93, demonstrating its effectiveness and potential.

As acoustic communication systems become increasingly common in our daily life, eavesdropping brings severe security and privacy risks. Current methods of acoustic eavesdropping either provide low resolution due to the use of sub-6 GHz frequencies, work only for limited words based on classification approaches, or cannot work through-wall because of the use of optical sensors. In this article, we presentmilliEar, a mmWave acoustic eavesdropping system that leverages the high-resolution of mmWave FMCW ranging and generative machine learning models to not only extract vibrations but to reconstruct the audio.milliEarcombines speaker vibration estimation with conditional generative adversarial networks to eavesdrop and recover high-quality audios (i.e., with no vocabulary constraints). We implement and evaluatemilliEarusing off-the-shelf mmWave radars deployed in different scenarios and settings. Evaluation results clearly show thatmilliEarcan accurately reconstruct the audio even at different distances, angles, and through the wall with different insulator materials. In addition, our subjective and objective evaluations demonstrate that the reconstructed audio has a strong similarity with the original audio.

NFC tag authentication is crucial for preventing tag misuse. Existing NFC fingerprinting methods use physical-layer signals, which incorporate tag hardware imperfections, for authentication purposes. However, these methods suffer from limitations such as low scalability for a large number of tags or incompatibility with various NFC protocols, hindering practical application. To address these issues, we propose a new NFC fingerprinting scheme called NFChain$^+$. Instead of sticking to the NFC resonant frequency, NFChain$^+$excavates the tag hardware uniqueness from the protocol-agnostic tag response signal using an agile and compatible frequency band of NFC to extract the tag fingerprint from a chain of tag responses over multiple frequencies. This significantly improves fingerprint scalability. However, extracting the desired fingerprint presents two challenges: fingerprint inconsistency under different configurations, and fingerprint variations due to the signal noise in generic readers. To overcome these challenges, we design an effective signal elimination method to remove the effect of device configurations and employ contrastive learning to reduce fingerprint variations for accurate tag authentication. We further cultivate a data augmentation strategy to save the cost of manually collecting fingerprint measurements for training the authentication model. Extensive experiments show that we can achieve as low as 3.4% FRR and 4.1% FAR for over 600 NFC tags.

Rotational speed is one of the important metrics to be measured for calibrating electric motors in manufacturing, monitoring engines during car repairs, detecting faults in electrical appliance and more. However, existing measurement techniques either require prohibitive hardware (e.g., high-speed camera) or are inconvenient to use in real-world application scenarios. In this paper, we propose,EV-Tach, a novel handheld rotational speed estimation system that utilizes emerging imaging sensors known as event cameras or dynamic vision sensors (DVS). The pixels of DVS work independently and trigger an event as soon as a per-pixel intensity change is detected, without global synchronization like conventional RGB cameras. Thus, its unique design features high temporal resolution and generates sparse events, which benefits the high-speed rotation estimation. To achieve accurate and efficient rotational speed estimation, a series of signal processing algorithms are specifically designed for the event streams generated by event cameras on an embedded platform. First, a new cluster-centroids initialization module is proposed to initialize the centroids of the clusters to address the issue that common clustering approaches are easy to fall into a local optimal solution without proper initial centroids. Second, an outlier removal module is designed to suppress the background noise caused by subtle hand movements and host devices vibrations. Third, a coarse-to-fine alignment strategy is proposed with an event stream alignment method to obtain angle of rotation and achieve accurate estimation for rotational speed in a large range. With these bespoke components,EV-Tachis able to extract the rotational speed accurately from the event stream produced by an event camera recording rotary targets. According to our extensive evaluations under controlled and practical experiment settings, the Relative Mean Absolute Error (RMAE) ofEV-Tachis as low as$0.3\%_{0}$, which is comparable to the state-of-the-art laser tachometer under fixed measurement mode. Moreover,EV-Tachis robust to subtle movement of user's hand and dazzling light outdoor, therefore, can be used as a handheld device under challenging lighting condition, where the laser tachometer fails to produce reasonable results. To speed up the processing ofEV-Tachand reduce its resource consumption on embedded devices, event stream is significantly downsampled by merging neighboring events while preserving its formation in spatial-temporal domain. At last, we implementEV-Tachon Raspberry Pi and the evaluation results show that the downsampling process preserves the high measurement accuracy while saving the computation speed and energy consumption by approximately 8 times and 30 times in average.

How to incentivize strategic workers using limited budget is a very fundamental problem for crowdsensing systems; nevertheless, since the sensing abilities of the workers may not always be known as prior knowledge due to the diversities of their sensor devices and behaviors, it is difficult to properly select and pay the unknown workers. Although the uncertainties of the workers can be addressed by the standardCombinatorial Multi-Armed Bandit(CMAB) framework in existing proposals through a trade-off between exploration and exploitation, we may not have sufficient budget to enable the trade-off among the individual workers, especially when the number of the workers is huge while the budget is limited. Moreover, the standard CMAB usually assumes the workers always stay in the system, whereas the workers may join in or depart from the system over time, such that what we have learnt for an individual worker cannot be applied after the worker leaves. To address the above challenging issues, in this paper, we first propose an off-lineContext-Aware CMAB-based Incentive(CACI) mechanism. We innovate in leveraging the exploration-exploitation trade-off in an elaborately partitioned context space instead of the individual workers, to effectively incentivize the massive unknown workers with a very limited budget. We also extend the above basic idea to the on-line setting where unknown workers may join in or depart from the systems dynamically, and propose an on-line version of the CACI mechanism. Specifically, by the exploitation-exploration trade-off in the context space, we learn to estimate the sensing ability of any unknown worker (even it never appeared in the system before) according to its context information. We perform rigorous theoretical analysis to reveal the upper bounds on the regrets of our CACI mechanisms and to prove their truthfulness and individual rationality, respectively. Extensive experiments on both synthetic and real datasets are also conducted to verify the efficacy of our mechanisms.

Introduction to the Special Section on Contact-free Smart Sensing in AloTArtificial Intelligence (AI) and the Internet of Things (IoT) are two powerful forces that have been reshaping our world in recent years.When they converge, they create a new field of AIoT that enables ubiquitous intelligence through the integration of smart algorithms and connected devices.One of the key enablers of AIoT is contact-free sensing, which leverages the availability of portable and highly integrated WiFi, radar, and sonar-style devices to monitor humans and environments without physical contact.This technology has transformed the traditional computer vision-based paradigms and opened up novel possibilities for data collection and analysis.However, contactfree sensing also poses new challenges and risks for AIoT applications.The dynamic and complex wireless environments require innovative solutions for efficient data processing and interpretation.The security and privacy issues of WiFi, radar, and sonar-enabled sensing devices also demand urgent attention, as they may expose sensitive information to malicious attacks.Therefore, it is imperative to explore the potential and pitfalls of contact-free sensing in AIoT and to develop effective strategies for ensuring the robustness and reliability of AIoT applications.This special issue is dedicated to highlighting the cutting-edge methods and latest research in the field of contact-free sensing, which leverages WiFi, radar, and sonar-style devices to monitor humans and environments without physical contact.The main focus of this issue is to explore the latest machine learning analytics to extract information from the sensory data and to investigate the potential risks and countermeasures to ensure the security and privacy of sensing devices.The call for papers attracted with 44 submissions and after a rigorous review, 18 papers have been accepted for this special issue.A brief summary of some papers in this special issue is presented in the following:In "Feasibility of Remote Blood Pressure Estimation via Narrow-band Multi-wavelength Pulse Transit Time, " the authors investigate the feasibility of estimating blood pressure (BP) via pulse transit time (PTT) in a novel remote single-site manner using a modified RGB camera.A narrowband triple band-pass filter makes it possible to measure the PTT between different skin layers, harvesting information from green and near-infrared wavelengths.They design a color-channel model and a novel channel-separation method to further resolve the inter-channel influence and band overlap.The results showed a good absolute Pearson's correlation coefficient between both MW PTT and systolic BP as well as diastolic BP, pointing to the feasibility of the proposed novel remote MW BP estimation via PTT.In "LiteWiSys: A Lightweight System for WiFi-based Dual-task Action Perception, " Sheng et al. propose a lightweight system named LiteWiSys that can simultaneously detect and recognize WiFi-based human actions.This work addresses two major drawbacks of existing methods: heavy

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications, New York City, NY, USA, 17-20 May 2023

Radio Frequency (RF)-based gait recognition has emerged as a promising technology to authenticate individuals in a pervasive and unobtrusive way. However, a fundamental challenge remains in collecting extensive data of the same user in the same environment. To address this challenge, this paper introduces XGait, a cross-modal gait recognition framework that does not require the prior deployment of RF devices or explicit data collection. The key idea is to leverage the signals of the Inertial Measurement Unit (IMU), which is widely available in modern mobile devices, to simulate the RF signals that would be generated if the same person walked near RF devices. Despite the straightforward idea, several technical challenges need to be addressed due to the diversity of RF devices, the intrinsic difference between IMU signals and RF signals, and the complexity of gait. First, we propose an RF spectrogram generation method to consistently extract essential RF gait data features across different RF signals. Secondly, we propose a generative network-enabled IMU-to-RF translation approach that accurately converts IMU data to RF data. Finally, we design an RF gait spectrogram-specific transformer model to further improve the recognition performance. We conduct a comprehensive evaluation of XGait, involving thirty subjects in three different environments, utilizing three RF devices and seven mobile devices. Experimental results show that XGait consistently achieves over 99% Top-3 accuracy in various scenarios.

Acoustic eavesdropping targeting private or confidential spaces is one of the most severe privacy threats. Soundproof rooms may reduce such risks, but they cannot prevent sophisticated eavesdropping, which has been an emerging research trend in recent years. Researchers have investigated such acoustic eavesdropping attacks via sensor-enabled side-channels. However, such attacks either make unrealistic assumptions or have considerable constraints. This paper introduces mmEcho, an acoustic eavesdropping system that uses a millimeter-wave radio signal to accurately measure the micrometer-level vibration of an object induced by sound waves. Compared with previous works, our eavesdropping method is highly accurate and requires no prior knowledge about the victim. We evaluate the performance of mmEcho under extensive real-world settings and scenarios. Our results show that mmEcho can accurately reconstruct audio from moving sources at various distances, orientations, reverberating objects, sound insulators, spoken languages, and sound levels.

Deep Learning (DL) has been widely adopted in almost all domains, from threat recognition to medical diagnosis. Albeit its supreme model accuracy, DL imposes a heavy burden on devices as it incurs overwhelming system overhead to execute DL models, especially on Internet-of-Things (IoT) and edge devices. Collaborative inference is a promising approach to supporting DL models, by which the data owner (the victim) runs the first layers of the model on her local device and then a cloud provider (the adversary) runs the remaining layers of the model. Compared to offloading the entire model to the cloud, the collaborative inference approach is more data privacy-preserving as the owner’s model input is not exposed to outsiders. However, we show in this paper that the adversary can restore the victim’s model input by exploiting the output of the victim’s local model. Our attack is dubbed Ginver 1: Generative model inversion attacks against collaborative inference. Once trained, Ginver can infer the victim’s unseen model inputs without remaking the inversion attack model and thus has the generative capability. We extensively evaluate Ginver under different settings (e.g., white-box and black-box of the victim’s local model) and applications (e.g., CIFAR10 and FaceScrub datasets). The experimental results show that Ginver recovers high-quality images from the victims.

Federated learning (FL) is a distributed model training paradigm that preserves clients’ data privacy. It has gained tremendous attention from both academia and industry. FL hyper-parameters (e.g., the number of selected clients and the number of training passes) significantly affect the training overhead in terms of computation time, transmission time, computation load, and transmission load. However, the current practice of manually selecting FL hyper-parameters imposes a heavy burden on FL practitioners because applications have different training preferences. In this paper, we propose, an automatic FL hyper-parameter tuning algorithm tailored to applications’ diverse system requirements in FL training. iteratively adjusts FL hyper-parameters during FL training and can be easily integrated into existing FL systems. Through extensive evaluations of for diverse applications and FL aggregation algorithms, we show that is lightweight and effective, achieving 8.48%-26.75% system overhead reduction compared to using fixed FL hyper-parameters. This paper assists FL practitioners in designing high-performance FL training solutions. The source code of is available at.

Due to the substantial computational cost of neural network training, adopting third-party models has become increasingly popular. However, recent works demonstrate that third-party models can be poisoned. Nonetheless, most model poisoning attacks require reference data, e.g., training dataset or data belonging to the target label, making them difficult to launch in practice. In this paper, we propose a reference data independent model poisoning attack that can (1) directly search for sensitive features with respect to the target label, (2) quantify the positive and negative effects of the model parameters on sensitive features, and (3) accomplish the training of poisoned model by our parameter selective update strategy. The extensive evaluation on datasets with a few classes and numerous classes show that the attack is (I) effective: the trigger input can be labeled as a deliberate class by the poisoned model with high probability; (II) covert: the performance of the poisoned model is almost indistinguishable from the intact model on non-trigger inputs; and (III) straightforward: an adversary only needs a little background knowledge to launch the attack. Overall, the evaluation results show that our attack achieves 95%, 100%, 81%, 96%, and 96% success rates on Cifar10, Cifar100, ISIC2018, FaceScrub, and ImageNet datasets, respectively.

This paper initiates the study for the basic primitive of distributed message dissemination in multi-hop wireless networks under a strong adversarial jamming model. Specifically, the message dissemination problem is to deliver a message initiating at a source node to the whole network. An efficient algorithm for message dissemination can be an important building block for solving a variety of high-level network tasks. We consider the hard non-spontaneous wakeup case, where a node only wakes up when it receives a message. Under the realistic SINR model and a strong adversarial jamming model that removes the budget constraint commonly adopted in previous work by the adversary, we present a distributed randomized algorithm that can accomplish message dissemination in$\mathscr{T}(O(D(\log n+\log R)))$time slots with a high probability performance guarantee, where$\mathscr{T}(U)$is the number of time slots in the interval from the beginning of the algorithm's execution that contains U unjammed time slots, n is the number of nodes in the network, D is the network diameter,$R$is the distance with respect to which the network is connected. Our algorithm is shown to be almost asymptotically optimal by lower bound$\Omega(D\log n)$for non-spontaneous message dissemination in networks without jamming.

Nowadays, more and more people are engaged in media creation and sharing for income. A common way to earn income is to upload media to an intermediary platform and then let the platform distribute some profits. However, intermediary platforms generally not only extract most of the profits, but also lack transparency in their operation, where owners lose direct control over the media. Nevertheless, individual sharing is not feasible for owners because each owner holds too little media to attract enough users independently. Blockchain is a solution to the above problem by gathering media from multiple owners without intermediaries. Though a lot of works have studied the use of blockchain for decentralized management of media data, many of them either did not consider sharing needs or tracing the illegal redistribution by malicious users. As for other works, most of them adopted symmetric digital watermarking in their blockchain networks, and thus fail to protect the rights of users who may be framed by malicious owners. Although asymmetric watermarking has been used by two existing works, the owner-side embedding pattern results in low owner-side efficiency. In view of this, we design a media sharing blockchain network in which the asymmetric fingerprinting (i.e., watermarking) with user-side embedding is introduced. Besides superior owner-side efficiency, our scheme also outperforms the above two ones in terms of TTP-free. Moreover, our scheme is designed to offer a user-friendly experience and support record traceability. The performance of our scheme is verified by both theoretical and experimental evaluations.

As acoustic communication systems become more common in homes and offices, eavesdropping brings significant security and privacy risks. Current approaches of acoustic eavesdropping either provide low resolution due to the use of sub-6 GHz frequencies, work only for limited words using classification, or cannot work through-wall due to the use of optical sensors. In this paper, we present MILLIEAR, a mmWave acoustic eavesdropping system that leverages the high-resolution of mmWave FMCW ranging and generative machine learning models to not only extract vibrations but to reconstruct the audio. MILLIEAR combines speaker vibration estimation with conditional generative adversarial networks to eavesdrop with unconstrained vocabulary. We implement and evaluate MIL-LIEAR using off-the-shelf mmWave radar deployed in different scenarios and settings. We find that it can accurately reconstruct the audio even at different distances, angles and through the wall with different insulator materials. Our subjective and objective evaluations show that the reconstructed audio has a strong similarity with the original audio.

With the increasing popularity of voice-based applications, acoustic eavesdropping has become a serious threat to users’ privacy. While on smartphones the access to microphones needs an explicit user permission, acoustic eavesdropping attacks can rely on motion sensors (such as accelerometer and gyroscope), which access is unrestricted. However, previous instances of such attacks can only recognize a limited set of pre-trained words or phrases. In this paper, we present AccEar, an accelerometer-based acoustic eavesdropping attack that can reconstruct any audio played on the smartphone’s loudspeaker with unconstrained vocabulary. We show that an attacker can employ a conditional Generative Adversarial Network (cGAN) to reconstruct high-fidelity audio from low-frequency accelerometer signals. The presented cGAN model learns to recreate high-frequency components of the user’s voice from low-frequency accelerometer signals through spectrogram enhancement. We assess the feasibility and effectiveness of AccEar attack in a thorough set of experiments using audio from 16 public personalities. As shown by the results in both objective and subjective evaluations, AccEar successfully reconstructs user speeches from accelerometer signals in different scenarios including varying sampling rate, audio volume, device model, etc.

Recently, recommender systems have achieved promising performances and become one of the most widely used web applications. However, recommender systems are often trained on highly sensitive user data, thus potential data leakage from recommender systems may lead to severe privacy problems.

COVID-19 is a severe global epidemic in human history. Even though there are particular medications and vaccines to curb the epidemic, tracing and isolating the infection source is the best option to slow the virus spread and reduce infection and death rates. There are three disadvantages to the existing contact tracing system: 1. User data is stored in a centralized database that could be stolen and tampered with, 2. User’s confidential personal identity may be revealed to a third party or organization, 3. Existing contact tracing systems [1][2] only focus on information sharing from one dimension, such as location-based tracing, which significantly limits the effectiveness of such systems.We propose a global COVID-19 information sharing and risk notification system that utilizes the Blockchain, Smart Contract, and Bluetooth. To protect user privacy, we design a novel Blockchain-based platform that can share consistent and non-tampered contact tracing information from multiple dimensions, such as location-based for indirect contact and Bluetooth-based for direct contact. Hierarchical smart contract architecture is also designed to achieve global agreements from users about how to process and utilize user data, thereby enhancing the data usage transparency. Furthermore, we propose a mechanism to protect user identity privacy from multiple aspects. More importantly, our system can notify the users about the exposure risk via smart contracts. We implement a prototype system to conduct extensive measurements to demonstrate the feasibility and effectiveness of our system.

IoT systems are revolutionizing our life by providing ubiquitous computing, inter-connectivity, and automated control. However, the increasing system complexity poses huge challenges for security as IoT devices are distributed, highly heterogeneous, and can directly interact with the physical environment. In IoT systems, bugs in device firmware, defects in network protocols, and design flaws in automation rules can lead to system breach or failure. The challenge gets even more escalated as the possible attacks may be chained together in a long sequence across multiple layers, rendering the existing vulnerability analysis frameworks inapplicable. In this paper, we present ForeSee, a model checking-based framework to comprehensively evaluate IoT system security. It builds a multi-layer IoT hypothesis graph by simultaneously modeling all of the essential components in IoT systems, including the physical environment, devices, communication protocols, and applications. The model checker can then analyze the generated hypothesis graph to validate system security properties or generate attack paths if there are any violations. An optimization algorithm is further introduced to reduce the computational complexity of our analysis. Our framework verifies hypothesis graphs with millions of nodes in less than 100 seconds. The illustrative case studies show that our framework can detect more potential threats than the existing approaches.

Collaborative learning allows multiple clients to train a joint model without sharing their data with each other. Each client performs training locally and then submits the model updates to a central server for aggregation. Since the server has no visibility into the process of generating the updates, collaborative learning is vulnerable to poisoning attacks where a malicious client can generate a poisoned update to introduce backdoor functionality to the joint model. The existing solutions for detecting poisoned updates, however, fail to defend against the recently proposed attacks, especially in the non-IID (independent and identically distributed) setting. In this article, we present a novel defense scheme to detect anomalous updates in both IID and non-IID settings. Our key idea is to realize client-side cross-validation, where each update is evaluated over other clients' local data. The server will adjust the weights of the updates based on the evaluation results when performing aggregation. To adapt to the unbalanced distribution of data in the non-IID setting, a dynamic client allocation mechanism is designed to assign detection tasks to the most suitable clients. During the detection process, we also protect the client-level privacy to prevent malicious clients from knowing the participations of other clients, by integrating differential privacy with our design without degrading the detection performance. Our experimental evaluations on three real-world datasets show that our scheme is significantly robust to two representative poisoning attacks.

While mobile apps often need to transmit sensitive information out to support various functionalities, they may also abuse the privilege by leaking the data to unauthorized third parties. This makes us question: Is the given transmission required to fulfill the app functionality? In this paper, we make the first attempt to automatically identify suspicious transmissions from app visual interfaces, including app names, descriptions, and user interfaces. We design and implement a novel framework called FlowIntent to detect nonfunctional transmissions at both software and network levels. During the exercising of the given apps, FlowIntent automatically detects privacy-sharing transmissions and determines their purposes by utilizing the fact that mobile users rely on visible app interface to perceive the functionality of the app at certain context. The characterizations of nonfunctional network traffic are then summarized to provide network level protection. FlowIntent not only reduces the false alarms caused by traditional taint analysis, but also captures the sensitive transmissions missed by widely-used taint analysis system TaintDroid. Evaluation using 2125 sharing flows collected from more than a thousand running instances shows that our approach achieves about 94 percent accuracy in detecting nonfunctional transmissions.

Internet of Things (IoT) has become the most promising technology for service automation, monitoring, and interconnection, etc. However, the security and privacy issues caused by IoT arouse concerns. Recent research focuses on addressing security issues by looking inside platform and apps. In this work, we creatively change the angle to consider security problems from a wireless context perspective. We propose a novel framework called IoTGaze, which can discover potential anomalies and vulnerabilities in the IoT system via wireless traffic analysis. By sniffing the encrypted wireless traffic, IoTGaze can automatically identify the sequential interaction of events between apps and devices. We discover the temporal event dependencies and generate the Wireless Context for the IoT system. Meanwhile, we extract the IoT Context, which reflects user's expectation, from IoT apps' descriptions and user interfaces. If the wireless context does not match the expected IoT context, IoTGaze reports an anomaly. Furthermore, IoTGaze can discover the vulnerabilities caused by the inter-app interaction via hidden channels, such as temperature and illuminance. We provide a proof-of-concept implementation and evaluation of our framework on the Samsung SmartThings platform. The evaluation shows that IoTGaze can effectively discover anomalies and vulnerabilities, thereby greatly enhancing the security of IoT systems.

LED-to-camera communication allows LEDs deployed for illumination purposes to modulate and transmit data which can be received by camera sensors available in mobile devices like smartphones, wearable smart-glasses, etc. Such communication has a unique property that a user can visually identify a transmitter (i.e., LED) and specifically receive information from the transmitter. It can support a variety of novel applications such as augmented reality through mobile devices, navigation using smart signs, fine-grained location specific advertisement, etc. However, the achievable data rate in current LED-to-camera communication techniques remains very low to support any practical application. In this paper, we present ColorBars, an LED-to-camera communication system that utilizes Color Shift Keying (CSK) to modulate data using different colors transmitted by the LED. It exploits the increasing popularity of Tri-LEDs (RGB) that can emit a wide range of colors. We show that commodity cameras can efficiently and accurately demodulate the color symbols. ColorBars ensures flicker-free and reliable communication even in the presence of inter-frame loss and diversity of rolling shutter cameras. We implement ColorBars on embedded platform and evaluate it with Android and iOS smartphones as receivers. Our evaluation shows that ColorBars can achieve a data rate of 7.7 Kbps on Nexus 5, 3.7 Kbps on iPhone 5S, and 2.9 Kbps on Samsung Note8. It is also shown that lower CSK modulations (e.g., four and eight CSK) provide extremely low symbol error rates (-3), making them a desirable choice for reliable LED-to-camera communication.

User's personal portable devices such as smartphone, tablet and laptop require continuous authentication of the user to prevent against illegitimate access to the device and personal data. Current authentication techniques require users to enter password or scan fingerprint, making frequent access to the devices inconvenient. In this work, we propose to exploit user's on-body wearable devices to detect their proximity from her portable devices, and use the proximity for continuous authentication of the portable devices. We present PCASA which utilizes acoustic communication for secure proximity estimation with sub-meter level accuracy. PCASA uses Differential Pulse Position Modulation scheme that modulates data through varying the silence period between acoustic pulses to ensure energy efficiency even when authentication operation is being performed once every second. It yields an secure and accurate distance estimation even when user is mobile by utilizing Doppler effect for mobility speed estimation. We evaluate PCASA using smartphone and smartwatches, and show that it supports up to 34 hours of continuous authentication with a fully charged battery.

The exponential growth of mobile devices has raised concerns about sensitive data leakage. In this paper, we make the first attempt to identify suspicious location-related HTTP transmission flows from the user's perspective, by answering the question: Is the transmission user-intended? In contrast to previous network-level detection schemes that mainly rely on a given set of suspicious hostnames, our approach can better adapt to the fast growth of app market and the constantly evolving leakage patterns. On the other hand, compared to existing system-level detection schemes built upon program taint analysis, where all sensitive transmissions as treated as illegal, our approach better meets the user needs and is easier to deploy. In particular, our proof-of- concept implementation (FlowIntent) captures sensitive transmissions missed by TaintDroid, the state-of-the-art dynamic taint analysis system on Android platforms. Evaluation using 1002 location sharing instances collected from more than 20,000 apps shows that our approach achieves about 91% accuracy in detecting illegitimate location transmissions.

LED-to-camera communication allows LEDs deployed for illumination purposes to modulate and transmit data which can be received by camera sensors available in mobile devices like smartphones, wearable smart-glasses etc. Such communication has a unique property that a user can visually identify a transmitter (i.e. LED) and specifically receive information from the transmitter. It can support a variety of novel applications such as augmented reality through mobile devices, navigation using smart signs, fine-grained location specific advertisement etc. However, the achievable data rate in current LED-to-camera communication techniques remains very low (≈ 12 bytes per second) to support any practical application. In this paper, we present ColorBars, an LED-to-camera communication system that utilizes Color Shift Keying (CSK) to modulate data using different colors transmitted by the LED. It exploits the increasing popularity of Tri-LEDs (RGB) that can emit a wide range of colors. We show that commodity cameras can efficiently and accurately demodulate the color symbols. ColorBars ensures flicker-free and reliable communication even in the presence of inter-frame loss and diversity of rolling shutter cameras. We implement ColorBars on embedded platform and evaluate it with Android and iOS smartphones as receivers. Our evaluation shows that ColorBars can achieve a data rate of 5.2 Kbps on Nexus 5 and 2.5 Kbps on iPhone 5S, which is significantly higher than previous approaches. It is also shown that lower CSK modulations (e.g. 4 and 8 CSK) provide extremely low symbol error rates (< 10--3), making them a desirable choice for reliable LED-to-camera communication.

The landscape of cyber security has been reformed dramatically by the recently emerging Advanced Persistent Threat (APT). It is uniquely featured by the stealthy, continuous, sophisticated and well-funded attack process for long-term malicious gain, which render the current defense mechanisms inapplicable. A novel design of defense strategy, continuously combating APT in a long time-span with imperfect/incomplete information on attacker's actions, is urgently needed. The challenge is even more escalated when APT is coupled with the insider threat (a major threat in cyber-security), where insiders could trade valuable information to APT attacker for monetary gains. The interplay among the defender, APT attacker and insiders should be judiciously studied to shed insights on a more secure defense system. In this paper, we consider the joint threats from APT attacker and the insiders, and characterize the fore-mentioned interplay as a two-layer game model, i.e., a defense/attack game between defender and APT attacker and an information-trading game among insiders. Through rigorous analysis, we identify the best response strategies for each player and prove the existence of Nash Equilibrium for both games. Extensive numerical study further verifies our analytic results and examines the impact of different system configurations on the achievable security level.

Compressive sensing (CS) has been viewed as a promising technology to greatly improve the communication efficiency of data gathering in wireless sensor networks. However, this new data collection paradigm may bring in new threats but few study has paid attention to prevent information leakage during compressive data gathering. In this paper, we identify two statistical inference attacks and demonstrate that traditional compressive data gathering may suffer from serious information leakage under these attacks. In our theoretical analysis, we quantitatively analyze the estimation error of compressive data gathering through extensive statistical analysis, based on which we propose a new secure compressive data aggregation scheme by adaptively changing the measurement coefficients at each sensor and correspondingly at the sink without the need of time synchronization. In our analysis, we show that the proposed scheme could significantly improve data confidentiality at light computational and communication overhead.

Though there exist ready-made DSRC/WiFi/3G/4G cellular systems for roadway communications, there are common defects in these systems for roadway safety oriented applications and the corresponding challenges remain unsolved for years, i.e., WiFi cannot work well in vehicular networks due to the high probability of packet loss caused by burst communications, which is a common phenomenon in roadway networks; 3G/4G cannot well support real-time communications due to the nature of their designs; DSRC lacks the support to roadway safety oriented applications with hard realtime and reliability requirements [1]. To solve the conflict between the capability limitations of existing systems and the ever-growing demands of roadway safety oriented communication applications, we propose a novel system design and implementation for realtime reliable roadway communications, aiming at providing safety messages to users in a realtime and reliable manner. In our extensive experimental study, the latency is well controlled within the hard realtime requirement (100ms) for roadway safety applications given by NHTSA [2], and the reliability is proved to be improved by two orders of magnitude compared with existing experimental results [1]. Our experiments show that the proposed system for roadway safety communications can provide guaranteed highly reliable packet delivery ratio (PDR) of 99% within the hard realtime requirement 100ms under various scenarios, e.g., highways, city areas, rural areas, tunnels, bridges. Our design can be widely applied for roadway communications and facilitate the current research in both hardware and software design and further provide an opportunity to consolidate the existing work on a practical and easy-configurable low-cost roadway communication platform.

As the advancement of sensing and networking technologies, participatory sensing has raised more and more attention as it provides a promising way enabling public and professional users to gather and analyze private data to understand the world. However, in these participatory sensing applications both data at the individuals and analysis results obtained at the users are usually private and sensitive to be disclosed, e.g., locations, salaries, utility usage, consumptions, behaviors, etc. A natural question, also an important but challenging problem is how to keep both participants and users data privacy while still producing the best analysis to explain a phenomenon. In this paper, we have addressed this issue and proposed M-PERM, a mutual privacy preserving regression modeling approach. Particularly, we launch a series of data transformation and aggregation operations at the participatory nodes, the clusters, and the user. During regression model fitting, we provide a new way for model fitting without any need of the original private data or the exact knowledge of the model expression. To evaluate our approach, we conduct both theoretical analysis and simulation study. The evaluation results show that the proposed approach produces exactly the same best model as if the original private data were used without leakage of the fitted model to any participatory nodes, which is a significant advance compared with the existing approaches [1-5]. It is also shown that the data gathering design is able to reach maximum privacy protection under certain conditions and be robust against collusion attack. Furthermore, compared with existing works under the same context (e.g., [1-5]), to our best knowledge it is the first work showing that not only the model coefficients estimation but also a series of regression analysis and model selection methods are reachable in mutual privacy preserving data analysis scenarios such as participatory sensing.