Sound eavesdropping poses serious threats to user privacy in daily mobile usage scenarios such as phone calls, voice messaging, and confidential meetings. Headphones are thus favored by mobile users as they provide physical sound isolation to protect audio privacy. However, our paper presents the first proof-of-concept system,Periscope, that demonstrates the vulnerabilities of headphone-plugged mobile devices. The system shows that audio-induced electromagnetic radiations (EMRs) from mobile devices' audio circuits can be exploited as an effective side channel in recovering the victim's audio sounds. Our theoretical analysis and feasibility studies further reveal that audio-induced EMRs are highly correlated with the device's audio inputs but suffer from signal distortions and ambient noises, making recovering audio sounds extremely challenging. To address this challenge, we develop signal processing techniques to clear noises and distortions, enabling EMRs to be converted back to audio sounds. Our attack prototype, comparable in size to hidden voice recorders, successfully recovers victims' private audio sounds with a word error rate (WER) as low as 7.44% across 12 mobile devices and 6 headphones. The recovery results are recognizable to natural human hearing and online speech-to-text tools. We also propose a software-based defense solution that mitigates this audio eavesdropping threat without requiring hardware modification.

Mobile devices have penetrated our daily life, and so have the fast chargers. However, while improving users' charging efficiency, fast chargers may pose severe security threats. In this work, we present a novel side-channel attack called ChargerWhisper, which exploits the acoustic signals generated by fast chargers to infer users' private information in an on-charging mobile device. In particular, when users perform different activities on the charging device, the fast charger will supply distinct amounts of power outputs to the mobile device. By conducting a deep investigation of the fast charger circuits, we find that the electronic components, i.e., inductors, capacitors, and transformers, vibrate at certain frequencies and generate high-frequency inaudible sounds. Meanwhile, the leaked sound frequencies are highly correlated with electric intensity flowing through these electronic components (charger's output power). To demonstrate the acoustic side-channel attack leveraging fast chargers, we present two specific exploitation scenarios: a website fingerprinting attack to identify users' on-browsing websites, and a PIN inference attack to infers the device's unlock PIN. Extensive evaluations are performed on off-the-shelf devices. The results show that both attacks are robust under diverse usage settings and achieve good performance in inferring users' privacy information. These findings highlight the essential need to address unnoticed privacy leakage risks associated with fast chargers.

Honeypots, which serve as active defense mechanisms, have historically played pivotal roles in cyberspace offensive and defensive countermeasure scenarios. However, with the advancement of honeypot recognition technologies, their effectiveness in real-world network defense has gradually diminished. In response, moving target defense (MTD) has recently solidified its position as a proactive cybersecurity strategy and a critical research frontier. MTD leverages heterogeneous, redundant deployments of service resources and randomization techniques to disrupt attack methods. However, despite their advantages, MTD systems face challenges related to high resource consumption. To address these limitations, we propose a moving target defense based on stochastic games and honeypots (GH-MTD) framework. This framework consists of four key modules: traffic detection, gaming, MTD, and honeynet. Firstly, malicious traffic is identified through a deep learning-based detection method. Secondly, a zero-sum game model is constructed to capture the decision-making dynamics between defenders and attackers in the context of moving target defense. Subsequently, a cross-scenario adaptive MTD module is designed to route different types of traffic to corresponding virtual server groups. Finally, a honeypot module is implemented to capture and analyze the specific attack behaviors of malicious actors. By integrating honeynet probes with real services and employing attack behavior analysis alongside internet protocol (IP) address redirection techniques, the GH-MTD system achieves a defense response that is both cost efficient and highly effective. Empirical evaluation reveals a 5.5-fold enhancement in attack diversion probability through benchmarking with service-oriented MTD architectures, while the capture rate surpasses that of conventional honeypots by 3.4 times. Particularly against real attackers, GH-MTD exhibits 5.6 times more captured packets and extends the time consumed by attackers by 1.5 times over that of standalone honeypots. In our experiments, we evaluate the architecture's performance against various attack methods, including automated scripts, manual attacks, and assaults by high-level penetration testers. The results demonstrate that the GH-MTD architecture performs exceptionally well, particularly in mitigating and countering advanced, sophisticated attacks, thereby demonstrating its effectiveness in modern network defense strategies.

With the rise of telemedicine and intelligent diagnostics, the efficiency and accuracy of healthcare services have been significantly enhanced. However, the highly sensitive nature of medical images makes protecting patient privacy during transmission and storage a critical challenge. In this paper, we propose a secure medical image encryption scheme based on cross-ring Josephus scrambling and two-dimensional cellular automata, designed to safeguard medical images. First, we introduce a novel two-dimensional chaotic map (2D-CICM) with an expanded parameter range to generate high-quality key sequences for encryption. Next, we design a cross-ring Josephus scrambling algorithm for pixel permutation, where the eliminated pixel is determined by both inter-ring and intra-ring step sizes. Following this, we develop a diffusion mechanism based on interaction rules defined by six types of two-neighbor structures within a cellular automaton framework. To enhance key sensitivity and image-specific security, we also incorporate the 512-bit hash value of the plaintext image to dynamically update the initial keys, ensuring that the encryption key sequences are unique for each image. Comprehensive security analyses and performance evaluations confirm that the proposed scheme provides strong encryption performance and effectively resists common attacks, while maintaining computational efficiency suitable for medical applications.

The local deployment of federated large language models (FLLM) has further advanced the development of edge intelligence. However, the resource constraints of end devices, device heterogeneity, and the non-independent and identically distributed (Non-IID) nature of data pose significant challenges to the application of FLLM. To address this issue, we propose an Adaptive Asynchronous Accelerated FLLM (Tri-AFLLM) algorithm to achieve the efficient utilization of limited resources and improve model accuracy in the edge computing (EC) scenarios. Specifically, Tri-AFLLM first ships an off-the-shelf LLM, i.e., CLIP, to each end device, keeping the backbone parameters frozen and updating only the parameters of the adapter containing two linear transformation layers by using momentum gradient descent (MGD). Next, a toy example is provided to illustrate the necessity of using different numbers of local iterations for heterogeneous devices in resource-constrained environments. Subsequently, the convergence bound of the Tri-AFLLM under a given resource budget is discussed. Then, we formulated the bound into a resource consumption minimization problem with the number of local iterations as the optimization variable under a given model accuracy to mitigate the contribution disparity of local models to the global aggregation. Finally, extensive experiments are conducted to validate the superiority of Tri-AFLLM in terms of resource consumption, model accuracy, and addressing the Non-IID problem.

Transferring important files, photos, and other sensitive data between laptops and smartphones has become a routine necessity in daily life. Device pairing acts as the most fundamental need to secure the communication channel between two unconnected devices. Traditional pairing methods leveraging PINs or QR codes require tedious human efforts in the pairing procedures to establish a shared communication key. Nevertheless, these designs are vulnerable to shoulder-surfing attacks in which attackers might record and replay the pairing credentials. It is preferred to have more intuitive pairing designs that minimize users’ overhead in the pairing process while providing secure keys for communication purposes. In this paper, we propose MIPair for laptop-phone pairing by leveraging magnetic induction (MI) signals. MIPair is based on a key observation that changes in the CPU workload of a device cause variations in internal current, thereby inducing changes in surrounding magnetic fields. Moreover, the trends of MI signal variations are highly correlated with CPU workload trends. Thus, users simply need to place a smartphone on the keyboard of a laptop. By randomly altering the workload of the laptop through a stimulation program, the smartphone can capture MI signals with similar changing trends, thereby converting them into similar bit sequences that form the basis of a symmetric key. We propose essential techniques to overcome challenges such as time asynchronization between two devices, unfixed state transition time in MI signal, and shared key distribution from the two similar bit sequences. Our real-world experiments demonstrate reliable pairing as well as robustness against common attacks and high randomness of the generated keys. When generating a 128-bit key, MIPair achieves a successful pairing rate as high as 98% and a usable pairing time of 8.35 seconds.

Internet of Things (IoT) networks have penetrated our daily life and industries. However, IoT devices are typically small-sized with constrained storage. Distributed storage systems are emerging as promising solutions to tackle such challenges. InterPlanetary File System (IPFS) is a desired framework enabling IoT devices to upload its data to a distributed cloud while returning a hash-ID for downloading and file-sharing purposes. Nevertheless, IPFS lacks of robust security design and is vulnerable to security threats such as data tampering, and data leakage. In particular, whenever device A’s file hash-ID is shared to an arbitrary device B, device A will fully lose the control over file. In other words, device B could further share it to anyone without device A’s agreements. To conquer the challenge, we propose a comprehensive design for securing the distributed IoT storage systems, named StorSec. Specifically, we design a new heterogeneous framework using an improved attribute encryption algorithm to eliminate the single-point performance bottleneck problem, which not only realizes fine-grained access control and ensures the security of data during transmission, but also improves the performance of key generation. Secondly, we design an anomaly detection algorithm, which is based on hashchain technology and combines the user privacy metadata stored on the blockchain to complete the verification process, effectively protecting the file hash identifier, ensuring access control to the file, and thus providing protection for the security and integrity of data storage. Furthermore, we design an auditing algorithm that helps the system in tracking malicious entities. Ultimately, the security and efficiency of the proposed scheme are evaluated by both security analysis and experimental results.

The escalating threat of hidden GPS tracking devices poses significant risks to personal privacy and security.Featured by their miniaturization and misleading appearances, GPS devices can be easily disguised in their surroundings making their detection extremely challenging.In this paper, we propose a novel side-channel-driven detection system, GPSBuster, leveraging electromagnetic radiation (EMR) emitted by GPS trackers.Our feasibility studies and hardware analysis reveal that unique EMR patterns associated with the tracker's operation, stemming from the quartz oscillator, local oscillator, and mixer in the Mixed-Signal on Chip (MSoC) system.Nevertheless, as a side-channel leakage, EMRs can be extremely weak and suffer from the ambient noise interference, rendering the detection impractical.To address these challenges, we develop the signal processing techniques with noise removals and a dual-dimensional folding mechanism to accumulate the spectrum energy and protrude the EMR patterns with high Signal-to-Noise Ratios (SNR).Our detection prototype, built with a portable HackRF One device, allows users to perform a scan-to-detect manner and achieves an overall success rate of 98.4% on top-10 selling GPS trackers under various testing cases.The maximum detection range is 0.61m.

Linux-based hypervisors in the cloud server suffer from an increasing number of vulnerabilities in the Linux kernel.To address these vulnerabilities in a timely manner while avoiding the economic loss caused by unplanned shutdowns, live patching schemes have been developed. Unfortunately, existing live patching solutions have failed to protect patches from post-deployment attacks. In addition, patches that involve changes to global variables can lead to practical issues with existing solutions. To address these problems, we present FortifyPatch, a tamper-resistant live patching solution for Linux-based hypervisors in cloud environments. Specifically, FortifyPatch employs multiple Granule Protection Tables from Arm Confidential Computing Architecture to protect the integrity of deployed patches. TrustZone Address Space Controller and Performance Monitor Unit are used to prevent the bypassing of the Patch via kernel code protection and timely page table verification. FortifyPatch is also able to patch global variables via well-designed data access traps.We prototype FortifyPatch and evaluate it using real-world CVE patches. The result shows that FortifyPatch is capable of deploying 81.5% of CVE patches. The performance evaluation indicates that FortifyPatch protects deployed patches with 0.98% and 3.1% overhead on average across indicative benchmarks and real-world applications, respectively.

While video recommendation has been studied extensively in regular PC and smartphone settings, such a topic has been rarely discussed in the virtual reality (VR) context so far. On the other hand, as the popularity of VR videos continues to soar, its recommendation will play a crucial part in providing suggestions and guiding users through a deluge of available content. Given this unmet need, in this work, we present Bere, a video recommender system tailored for VR. Our approach leverages viewers' behavioral responses as they engage with VR videos to infer their preferences and thus make future recommendations. We integrate these new behavioral user-video interaction measures into the mainstream recommendation framework and renovate the graph learning-based paradigm to accommodate the new changes. The recommender system is further empowered with a novel domain adaptation approach named CMCCDA to address the data scarcity problem for model training. We also develop an energy-efficient adaptive encoding scheme to reduce the energy consumption on the VR device. We collect a behavioral dataset for video recommendation in VR and demonstrate through extensive evaluation that Bere significantly outperforms state-of-the-art schemes by up to 68.0% in precision and up to 28.8% in ranking quality.

Reckless driving, primarily speeding, poses a significant threat to road safety. While measures such as speed cameras and traffic patrol officers aim to deter speeding, their effectiveness is limited by coverage constraints and their inability to monitor areas beyond their immediate vicinity. To address these challenges, we introduce a crowd-sensing framework for speed violation detection with privacy preservation. This framework enables individuals to identify speeding vehicles while ensuring the privacy of involved parties. The framework comprises three key entities: the Speed Reporter (SR), the Speed Violator (SV), and the Ticket Issuer (TI). By leveraging this approach, the detection coverage of speed violators is extended, providing near real-time monitoring of reckless driving incidents. To correctly identify the speeding vehicles for our framework, we have utilized YOLO (You Only Look Once) to develop a real-time object detection model, for vehicle and license plate detection. The experimental results of our models show the effectiveness and practicality of our proposed framework.

The proliferation of machine learning (ML) applications has given rise to a new and popular data marketplace paradigm. These marketplaces facilitate ML model requesters in obtaining data from data owners to train their desired models. To mitigate the privacy concerns of data owners, federated learning (FL) has been introduced, enabling collaborative model training without raw data trading. Furthermore, researchers have incorporated differential privacy (DP) techniques into FL, resulting in differentially private federated learning (DPFL) to enhance privacy preservation. However, existing designs of DPFL-based data marketplaces consider a simplified but unrealistic scenario where the model requester holds dominant market power, and data owners cannot set their own prices. In this work, we propose a novel DPFL-based data marketplace that accommodates both price-taking and price-setting data owners. We model the interactions among the model requester and these two types of data owners as a three-stage Stackelberg game, focusing on maximizing the model requester's profit. We rigorously establish that the formulated game is a convex game with a unique subgame perfect equilibrium. Moreover, we devise iterative algorithms to determine the equilibrium strategies for the model requester and price-setting data owners. Notably, our algorithms allow data owners to operate without requiring complete information about the model requester or other data owners. Numerical experiments demonstrate the superiority of our proposed three-stage framework in terms of the model requester's profitability compared to scenarios where only price-taking data owners are involved. Furthermore, we reveal that price competition among price-setting data owners reduces equilibrium market prices.

Internet of Things (IoT) devices play essential roles in both industry and daily scenarios. However, unlike smartphones and computers, IoT devices typically lack conventional user interfaces (UIs) such as keyboards and touchscreens. It renders the traditional user authentication designs, e.g., PINs and patterns, inapplicable. In this article, we proposeTouchAccessthat enables users to unlock an arbitrary IoT device by applying a simple touch. Our design is motivated by the key observation that IoT devices unavoidably generate electromagnetic emanations (EMM) while they are functioning. When the user touches the device, it causes time-varying coupling between those two and generates unique EMMs. Our feasibility studies further reveal that thesehuman-induced EMMsare distinct and strongly correlated with the circuitry properties of the user and the device, but are susceptible to environmental EM noises, thus lowering the authentication accuracy. To address this challenge, we develop signal processing techniques with a Siamese network learning scheme that clears the ambient electromagnetic (EM) noises, extracts robust signal features, and builds noise-resistant classifiers, enabling users to be correctly recognized. A significant advantage ofTouchAccessis that it requires only a low-cost analog-to-digital converter (ADC) to sense the EM signal. We implementTouchAccesson commercial off-the-shelf (COTS) IoT devices, which vary significantly in terms of UIs, sizes, and hardware designs. The performance evaluations show thatTouchAccessachieves an average authentication accuracy as high as 97.85%.

The popularity of IoT devices has penetrated our daily life while posing new challenges in user authentication. Today’s solutions, e.g., passwords, fingerprints, and FaceIDs, primarily rely on specialized sensors or user interfaces to collect user’s identification information, which may not universally exist on heterogeneous IoT devices. In this paper, we propose a novel user authentication method that exploits the Electromagnetic (EM) emanations radiated from IoT devices. Our design is motivated by the observation that human touches on the IoT device can lead to time-varying coupling between these two. Consequently, it impacts the device’s EM emanations that can be picked up by its inertial ADC (analog-to-digital converter) interfaces. We ask the user to tap on the device rhythmically following a self-determined melody, such that the human-coupled EM emanations vary accordingly. We thus extract the rhythm pattern as the user’s secure password named EM-Rhythm . To examine its effectiveness, EM-Rhythm is implemented on a wide range of IoT devices. We show that our scheme achieves authentication accuracy as high as 98.67% with less than three login attempts. Besides, it is robust against various types of attacks and maintains stable performances under various settings. EM-Rhythm also exhibits satisfactory usability in terms of memorability and time consumption.

Most terminal devices authenticate users only once at the time of initial login, leaving the terminal unprotected during an active session when the original user leaves it unattended. To address this issue, continuous authentication has been proposed by automatically locking the terminal after a period of inactivity. However, it does not fully eliminate the risk of unauthorized access before the session expires. Recent research has also investigated the feasibility of using physiological and behavioral patterns as biometrics. This study presents a novel two-factor continuous authentication that explores a new form of signal called human-induced electric potential captured by wearables in contact with the user’s body. By analyzing this signal, we can determine the time of user-terminal interactions and compare it with information recorded by the terminal’s OS. If the original user remains on the same terminal, the two-source readings would match. Additionally, the proposed scheme includes an extra layer of protection by extracting terminal’s physical fingerprints from the human-induced electric potential to defend against advanced mimicry attacks. To test the effectiveness of our design, a low-cost wearable prototype is developed. Through extensive experiments, it is found that the proposed scheme has a low error rate of 2.3%, with minimal computational and energy requirements.

Software-Defined Networking (SDN) switches typically have limited ternary content addressable memory (TCAM) that caches the flow entries on the data plane. The scarcity and strong resource competitiveness of TCAM space put the flow tables at the risk of malicious Distributed Denial-of-Service (DDoS) attacks. In this paper, we propose LtRFT, a Learning-To-Rank (LtR) based scheme for mitigating the low-rate DDoS attacks targeted at flow tables. LtRFT consists of three modules:monitor,ranker, andmitigator.Monitormanages the flow table status and sends alerts to other modules after detecting attacks.Rankermodels the attack mitigation problem as a flow entry ranking task, and ranks malicious flows with a high eviction priority using a pairwise-based LtR algorithm. Themitigatorfrees up the flow table space by deleting malicious flow entries according to the flow entry ranking sequence generated byranker. We introduce LtR to network attack detection innovatively and use both classification and information retrieval metrics to describe and evaluate LtRFT. Extensive experiments were conducted to validate the effectiveness and robustness of LtRFT in detecting and mitigating the low-rate data plane DDoS attacks. LtRFT can detect malicious attack flows with an accuracy of over 96%, and can reduce the attack flow duration by 97.7% with an average extra latency of 0.5 seconds, which proves that LtRFT is practicable in SDN deployments.

Mobile crowdsourcing has emerged as a promising paradigm that applies the principle of crowdsourcing to perform tasks of mobility requirement. Due to the openness of mobile crowdsourcing, workers may yield low-quality task answers. To alleviate this problem, substantial efforts have been devoted to elicit truthful data from workers. On the other hand, to facilitate task assignment, workers are required to upload the platform their profiles, such as locations and expertise. Therefore, task assignment outcomes and thus mobile crowdsourcing service accuracy is subject to the quality of workers’ self-reported profiles. In this paper, we leverage incentive design to motivate workers to honestly reveal both task answers and their profiles. The challenge is to design one incentive payment for truth elicitation in two kinds of submissions. For this, we first derive the sufficient and necessary conditions for answer truthfulness and profile truthfulness separately. We then construct an incentive optimization problem that incorporates these conditions as constraints. Its optimal solution lists the payment to each worker that elicits answers and profiles jointly. Our proposed mechanism, with a formally proved bounded approximation ratio, ensures that truth-telling is a Bayesian Nash equilibrium. We prototype the mechanism and conduct a series of experiments that involve 30 volunteers to validate the efficacy and efficiency of the proposed mechanism.

In the wake of the Web 2.0, crowdsourcing has emerged as a promising approach to maintain a flexible workforce for human intelligence tasks. To stimulate worker participation, many reverse auction-based incentive mechanisms have been proposed. Designing auctions that discourage workers from cheating and instead encouraging them to reveal their true cost information has drawn significant attention. However, the existing efforts have been focusing on tackling individual cheating misbehaviors, while the scenarios that workers strategically form collusion coalitions and rig their bids together to manipulate auction outcomes have received little attention. To fill this gap, in this work we develop a$(t,p)$-collusion resistant scheme that ensures no coalition ofweighted cardinality$t$can improve its group utility by coordinating the bids at a probability of$p$. This paper takes into account the unique features of crowdsourcing, such as diverse worker types and reputations, in the design. The proposed scheme can suppress a broad spectrum of collusion strategies. Besides, desirable properties, including$p$-truthfulness and$p$-individual rationality, are also achieved. To provide a comprehensive evaluation, we first analytically prove our scheme's collusion resistance and then experimentally verify our analytical conclusion using a real-world dataset. Our experimental results show that the baseline scheme, where none of the critical properties is guaranteed, costs up to 20.1 times the optimal payment in an ideal case where no collusion exists, while our final scheme is merely 4.9 times the optimal payment.

Software defined networking (SDN), a highly regarded architecture, enhances the programmability and manageability of the network by decoupling the data plane and the control plane. It has emerged to bring more possibilities to the Internet, but at the same time, its inherent shortcomings have become a pool for malicious attackers. Low-rate denial of service (LDoS) attacks, a variant of denial of service attacks, also pose a threat to the SDN architecture. In this article, we replicate LDoS attacks for the SDN data plane and propose a detection and mitigation framework called GASF-IPP based on multiple traffic and IP-port data by analyzing the network anomalies. By leveraging the OpenFlow protocol, the traffic of switches is monitored. We use Gramian angular summation field (GASF) transformation based on timing analysis to analyze the traffic and combine it with other features to determine whether an attack has occurred. By locating the attacker and the victim, flow rules can be constructed for mitigation. Experiments prove that our proposed framework is correct and effective, the detection and mitigation module can perform real-time work with a low false positive rate (FPR) and respond in average 6.77 s.

Mobile crowd sensing (MCS) arises as a promising data collection paradigm that leverages the power of ubiquitous mobile devices to acquire rich information regarding their surrounding environment. In many location-based sensing tasks, workers are required to associate their sensing reports with corresponding geographic coordinates. Such information leaves a trail of worker's historical location record which thus poses a severe threat to their location privacy. On the other hand, individual workers may perceive location privacy differently. Instead of following conventional solutions that aim to perfectly hide user privacy, this paper adopts a novel alternative approach. Auser-centriclocationprivacytrading framework, called ULPT, is constructed to facilitate location privacy trading between workers and the platform. Each worker can decide how much location privacy to disclose to the platform in an MCS task based on its own location privacy leakage budget$\xi$. The higher$\xi$is, the more privacy its reported location discloses. Accordingly, it receives higher payment from the platform as compensation. Besides, ULPT enables the platform to select a suitable set of winning workers to achieve desirable MCS service accuracy while taking into account of its budget limit and worker privacy requirements. For this purpose, a heuristic algorithm is devised with a bounded optimality gap. As formally proved in this manuscript, ULPT guarantees a series of nice properties, including$\xi$-privacy,$(\alpha, \beta)$-accuracy,budget feasibility. Moreover, both rigorous theoretical analysis and extensive simulations are conducted to evaluate tradeoffs among these three.

Cyber-physical systems revolutionize how we interact with physical systems. Smart grid is a prominent example. With new features such as fine-grained billing, user privacy is at a greater risk than before. For instance, a utility company () can infer users’ (fine-grained) usage patterns from their payment. The literature only focuses on hiding individual meter readings in bill calculation. It is unclear how to preserve amount privacy when the needs to assert that each user has settled the amount as calculated in the bill.

This study presents Periscope, a novel side-channel attack that exploits human-coupled electromagnetic (EM) emanations from touchscreens to infer sensitive inputs on a mobile device. Periscope is motivated by the observation that finger movement over the touchscreen leads to time-varying coupling between these two. Consequently, it impacts the screen's EM emanations that can be picked up by a remote sensory device. We intend to map between EM measurements and finger movements to recover the inputs. As the significant technical contribution of this work, we build an analytic model that outputs finger movement trajectories based on given EM readings. Our approach does not need a large amount of labeled dataset for offline model training, but instead a couple of samples to parameterize the user-specific analytic model. We implement Periscope with simple electronic components and conduct a suite of experiments to validate this attack's impact. Experimental results show that Periscope achieves a recovery rate over 6-digit PINs of 56.2% from a distance of 90 cm. Periscope is robust against environment dynamics and can well adapt to different device models and setting contexts.

Pedestrian trajectory prediction is essential for collision avoidance in autonomous driving, which can help autonomous vehicles have a better understanding of traffic environment and perform tasks such as risk assessment in advance. In this paper, pedestrian path prediction at a time horizon of 2s for autonomous driving is systematically investigated using waiting/crossing decision model (W/CDM) and modified social force model (MSFM), and the possible conflict between pedestrians and straight-going vehicles at an un-signalized crosswalk is focused on. First of all, a W/CDM is efficiently developed to judge pedestrians' waiting/crossing intentions when a straight-going vehicle is approaching. Then the humanoid micro-dynamic MSFM of pedestrians who have been judged to cross is characterized by taking into account the evasion with conflicting pedestrians, the collision avoidance with straight-going vehicles, and the reaction to crosswalk boundary. The influence of pedestrian heterogeneous characteristics is considered for the first time. Moreover, aerial video data of pedestrians and vehicles at an un-signalized crosswalk is collected and analyzed for model calibration. Maximum likelihood estimation (MLE) is proposed to calibrate the non-measurable parameters of the proposed models. Finally, the model validation is conducted with two cases by comparing with the existing methods. The result reveals that the integrated method (W/CDM-MSFM) outperforms the existing methods and accurately predicts the path of pedestrians, which can give us great confidence to use the current method to predict the path of pedestrian for autonomous driving with significant accuracy and highly improve pedestrian safety.

While traffic modeling and prediction are at the heart of providing high-quality telecommunication services in cellular networks and attract much attention, they have been approved as an extremely challenging task. Due to the diverse network demand of Internet-based apps, the cellular traffic from an individual user can have a wide dynamic range. Most existing methods, on the other hand, model traffic patterns as probabilistic distributions or stochastic processes and impose stringent assumptions over these models. Such assumptions may be beneficial at providing closed-form formula in evaluating prediction performances, but fall short for practice use. In this paper we propose STEP, aspatio-temporal fine-granular user trafficprediction mechanism for cellular networks. A deep graph convolution network, called GCGRN, is constructed. It is a novel combination of the graph convolution network (GCN) and gated recurrent units (GRU), which exploits graph neural network to learn an efficient spatio-temporal model from a user’s massive dataset for traffic prediction. The prototype of STEP has been implemented. Extensive experimental results demonstrate that our model outperforms the state-of-the-art time-series based approaches. Besides, STEP merely incurs mild energy consumption, communication overhead and system resource occupancy to mobile devices. Moreover, NS-3 based simulations validate the efficacy of STEP in reducing session dropping ratio in cellular networks.

Wearable devices that capture user's rich information regarding their health conditions and daily activities have unmet pairing needs. Today's solutions, which primarily rely on human involvement, are cumbersome, error-prone, and do not scale well. Despite some prior efforts trying to fill this gap, they either rely on some sophisticated sensors, such as electromyogram (EMG) or electrocardiogram (ECG) pads that may not universally exist, or non-trivial design of communication transceivers that cannot be found easily on current commercial devices. Therefore, a pairing scheme for wearable devices that is secure, practical, and convenient is in dire need. In this paper, we propose a novel approach that leverages ambient radio frequency (RF) noise. Our design is based on a key observation that received RF noise power measured in the logarithmic scale at different parts of a human body surface experience the same variation trend, whereas those from different human bodies or off the body are distinct. Wearables make use of the observed noise as the entropy source for the proposed pairing protocol. Extensive experiments show that our scheme has an equal error rate (EER) as low as 1.4% for pairing. Its key generation rate reaches 138 bits/sec, which beats so-far existing pairing schemes. Besides, our scheme can be efficiently executed within 0.97 s. Its incurred energy consumption is as low as 0.27 J for the entire pairing procedure.

Mobile crowd sensing (MCS) is a technique where sensing tasks are outsourced to a crowd of mobile users. Since most of sensing tasks are location-dependent, workers are required to embed their locations into sensing reports, which incurs location privacy vulnerabilities. Realizing that workers perceive their location privacy differently, in this work we construct an auction-based trading market, facilitating location privacy trading between workers and the platform. Each worker can decide how much location privacy to disclose to the platform based on its own location privacy leakage budget $\xi$. The higher $\xi$ is, the less secrecy its reported location preserves. As a result, it receives higher payment from the platform as a compensation to its privacy loss. Besides, our mechanism enables the platform to select a suitable set of winning workers to achieve desirable service accuracy. For this purpose, a heuristic algorithm is devised, with polynomial-time complexity and bounded optimality gap. As formally proved in this manuscript, our proposed mechanism guarantees a series of nice properties, including $\xi$-privacy, $(\alpha,\beta)$accuracy, and budget feasibility.

Preceding target vehicle (PTV) motion recognition play a pivotal role in autonomous vehicles. Motion states such as yaw rate, longitudinal and lateral velocity are critical for ego vehicle decision-making and control. However, lateral states of a PTV can hardly be measured directly by common onboard sensors and the PTV lateral state estimation has been seldom addressed in existing literatures. In this paper, a novel estimation scheme based on multiple neural network ensemble is proposed for PTV lateral state estimation. First, PTV lateral kinematics is presented based on vehicle-road relationship and a novel PTV lateral motion model is constructed to interpret the PTV lateral motion. Then, neural network observer with the PTV lateral kinematics as prior knowledge is designed and training data are collected in simulation environment. The neural network observer is trained using Levenberg-Marquardt backpropagation with Bayesian regularization (LMBR) to improve the generalization capability. Finally, to further improve the performance of the neural network estimation method, multiple neural network observers are integrated by weighted averaging strategy. The effectiveness of proposed approach is verified through hardware-in-the-Ioop (HiL) experiments conducted in designed verification scenarios, and compared with model-based method and other three learning methods. The experiment results reveal that the proposed method outperforms other typical methods and achieves accurate estimation of the PTV lateral states.

Wearable devices that capture users' rich information regarding their daily activities have unmet authentication needs. Today's solutions, which primarily rely on indirect authentication mechanisms via users' smartphones, thus cumbersome and susceptible to adversary intrusions. Even though there have been some efforts trying to fill this gap, they either rely on some superior sensors, such as cameras and electrocardiogram (ECG) pads, or are awkward to use, e.g., users are asked to perform some pre-defined movement/gesture for authentication. Therefore, an authentication mechanism for wearable devices that is accurate, robust, light-weight and convenient is in dire need.