Radio Frequency Fingerprinting (RFF) exploits inherent hardware-level imperfections of wireless transmitters as unclonable identifiers for device identification. These unique signatures, concealed in transmitted signals, inevitably experience complex distortions during wireless propagation (i.e., coupled with ambient noise and channel fading), making it extremely challenging for reliable extraction. Despite substantial research efforts dedicated to advancing effective fingerprint extraction techniques, current approaches still struggle in handling fingerprint robustness under distance variations, leading to severe SNR fluctuations and complex multipath effects. To address this gap, we propose the first unsupervised framework for distance-invariant radio frequency fingerprinting, eliminating dependence on labeled target domain data. Specifically, we first preprocess raw RF samples by confining them within a specified variation range and filtering noisy high-frequency components while avoiding aliasing. For source domain data, we then propose a set of physics-inspired data augmentation techniques designed to emulate realistic wireless signal propagation effects. Building on this, we introduce a dual alignment contrastive learning method to explicitly decouple identity-discriminative features, ensuring the model focuses on device-specific traits. Furthermore, we incorporate a pseudo-labeling-based domain adaptation module to refine the model for the unlabeled target domain, enhancing its generalization to unseen distances. Extensive experiments on public datasets show that our method achieves the identification accuracy outperforming state-of-the-art approaches by 40%, while maintaining computational efficiency suitable for edge deployment.

Trajectory similarity query can retrieve a set of trajectories similar to the user's query from the database and is widely used in various fields such as travel recommendations. Previous studies mainly focused on accelerating trajectory similarity search in plaintext. However, with the increasing concern about privacy protection in outsourced cloud environments, conducting trajectory similarity queries while preserving privacy becomes a significant challenge. This paper proposes efficient privacy-preserving top-$k$and range similarity queries over trajectory data. We leverage Discrete Synchronous Euclidean Distance (DSED) to measure the spatio-temporal similarity of trajectory data, and employ a filter-then-refine strategy to enhance efficiency. Specifically, Hilbert curve-based filtering is first applied to exclude a large portion of dissimilar trajectories, followed by homomorphic encryption-based refinement to retrieve precise results. Security analysis demonstrates that our schemes protect the privacy of trajectory data, query requests, and query results. Finally, extensive experimental results indicate that the proposed methods achieve a trade-off between data availability and privacy, achieving over 99% average precision while initially filtering out 90% of dissimilar trajectories, and improving query efficiency by at least an order of magnitude.

Physical layer authentication (PLA), which leverages device-specific physical layer features to achieve information-theoretic security with low complexity, offers a hardware-rooted security solution for next-generation Internet of Things (IoT) networks. While existing PLA approaches primarily rely on passive extraction of inherent hardware-induced radio frequency features, they remain vulnerable to adversarial spoofing that replicates legitimate radio frequency fingerprints (RFF). To address this critical vulnerability, we propose an active PLA framework that employs a challenge–response protocol to embed session-specific perturbation into each transmission. Upon receiving a nonce from the receiver, the legitimate transmitter generates a hash value and embeds a corresponding in-phase and quadrature (IQ) imbalance-based perturbation into the baseband signal. This design conceals inherent hardware-specific RFF and injects dynamic, unpredictable fingerprints that vary across sessions and are resilient to forgery. At the receiver, authentication is performed using a learning-based method that combines a CNN-based feature extractor with a lightweight logistic regression classifier trained on augmented samples. Extensive simulations demonstrate that the proposed framework achieves high authentication accuracy under both static and dynamic channel conditions, while effectively resisting advanced spoofing attacks, including GAN-based impersonation. These results confirm the robustness, generalization capability, and applicability of the proposed scheme for secure IoT communications.

Accurate detection of low-frequency DNA variants (below 1%) is essential in diverse biological and clinical contexts, yet remains fundamentally constrained by the high intrinsic error rates of next-generation sequencing technologies. Although unique molecular identifiers (UMIs) have significantly mitigated these errors by uniquely indexing original template molecules, their efficacy is compromised by UMI collisions and by artifacts introduced during polymerase chain reaction (PCR) amplification and sequencing, which collectively engender false-positive variant calls. Here, we present AFUMIC, an alignment-free UMI clustering framework that systematically addresses these limitations through collision-resilient UMI grouping and a consensus quality score (CQS)-guided strategy for high-fidelity consensus sequence generation. AFUMIC reduces singleton families, enhances clustering precision, and maximizes data retention, yielding 7.27-fold and 3.84-fold increases in single-strand consensus sequence and duplex consensus sequence output, respectively, compared to Du Novo. It further decreases the per-base error rate from $3.01 \times 10^{-4}$ to $2.10 \times 10^{-5}$ and raises the proportion of error-free positions from 45.27% to 99.85%, enabling confident detection of variants at variant allele frequencies as low as $1.0 \times 10^{-5}$. Notably, AFUMIC exhibits superior computational efficiency, rendering it well-suited for high-throughput analysis of UMI-tagged libraries in large-scale genomic studies. Collectively, AFUMIC represents an efficient methodology for ultrasensitive variant detection and establishes a broadly applicable and computationally efficient framework for error-corrected sequencing that can be readily deployed in both clinical diagnostics and large-scale genomic research.

How to effectively diagnose and mitigate database performance anomalies remains a significant concern for modern database systems. Manually identifying the root causes of the anomalies is a labor-intensive process and significantly relies on professional experience. Meanwhile, existing work on automatic database diagnosis mainly focuses on detecting anomalous performance metrics or system log. These solutions lack the power to pinpoint detailed issues such as bad queries or problematic operators, which are indispensable for most database troubleshooting processes. In this paper, we propose OpDiag, a diagnosis framework that attributes database performance anomalies to query operators. In this framework, we first construct models offline to represent the relationship between query operators, performance metrics, and anomalies. These models can capture query plan features and support ad-hoc queries and schemas. Then, through feature attribution on these models during online diagnosis, OpDiag can effectively identify critical anomalous metrics and further trace back to suspicious queries and operators. This can provide concrete guidance for subsequent steps in anomaly mitigation. We applied OpDiag to both synthetic benchmark and real industry cases from ZTE Corporation. Empirical studies prove that OpDiag can accurately localize anomalous queries and operators, thus reducing human efforts in diagnosing and mitigating database performance anomalies.

The widespread adoption of wrist wearables featuring a range of sensors has led to a substantial user base. Among these sensors, Photoplethysmography (PPG) sensors have gained prominence for their affordability and non-intrusive nature, particularly in the context of vital signs monitoring. However, PPG sensors, with their potential for gesture recognition, also have the ability to extract sensitive private information from users. As the authentication method for many critical scenarios, once a PIN is compromised, it can lead to unbearable consequences. In this study, we introduce PPGLogger, a new side-channel attack that leverages PPG sensors for PIN inference. PPGLogger effectively separates signals originating from heartbeats and keystrokes to minimize noise interference. Additionally, we devise a keystroke detection technique capable of identifying and segmenting individual keystroke signals within the waveforms. For the keystroke inference component, we employ an Rocket-based classifier to achieve precise keystroke recognition. Through extensive real-world experiments, our findings are compelling. PPGLogger achieves an impressive accuracy rate of 76.3% in recognizing 10 digits. Furthermore, we demonstrate that PPGLogger can attain over 50% accuracy in classifying single keystrokes with a minimal dataset of just 28 samples, equivalent to merely 7 PIN entries. This underscores the efficacy and potential threat posed by PPGLogger in compromising user security.

Optical image stabilization (OIS), powered by a special micro-electromechanical structure in the camera lenses to compensate for the optical distortion caused by camera shakes, has become an indispensable feature in many smartphones. However, we discover that this seemingly benign component can be exploited to eavesdrop on nearby audio signals, posing a significant threat to people's privacy during conversations or phone calls. Specifically, the OIS component can be influenced by external acoustic stimuli leading to slight vibrations, and at the same time, the coil and magnetized components inside the OIS induce electromagnetic leakage as they vibrate, according to Faraday's Law of Electromagnetic Induction. This electro-magnetic leakage contains voice information that can be used to recover the audio signals if intercepted by individuals with malicious intent. Inspired by the above discovery, we propose OISMic, a new acoustic eavesdropping attack that takes advantage of sound-induced OIS vibrations on smartphones. Unlike other existing acoustic eavesdropping attacks, eavesdropping exploiting OIS vibrations not only overcomes the constraints imposed by system permissions for many sensor-based approaches but is also immune to ultrasonic jammer that hinders the methods relying on microwave or light reflections to sense sound-induced vibrations. To execute this non-trivial attack in practical scenarios, we developed a prototype circuit that has a compact design capable of capturing the electromagnetic leakage caused by OIS vibrations. After converting the collected leaked electromagnetic signals into audio signals, a software-based phase-locked loop (PLL) method is developed to enhance the representation of voice components. Meanwhile, to reconstruct the weak audio signals, we also designed a diffusion-based neural network to learn the distribution of electromagnetic noise within the audio spectrum. Extensive experiments indicate that OISMic can accurately reconstruct voice under various scenarios, achieving an average word correct rate of 90.57 % across different devices.

Anomaly diagnosis is vital to the performance of online transaction processing (OLTP) systems. In the meanwhile, machine learning techniques can reason complex relationships beyond human abilities and perform well on such problems. However, they rely on a large number of training samples for anomalies, which are in serious shortage in both industry and academia due to the difficulty of collection. The problem raises the demand of a benchmark for anomaly reproduction and data collection. In this paper, we propose DBPA, a benchmark for transactional database performance anomalies. Specifically, we identify nine common anomalies rooted in the diverse influence factors. For each anomaly, we carefully design a reproduction procedure, which consists with its root cause in real-world databases. With the reproduction procedures, users can easily generate a dataset in a new environment and extend new anomaly types. For compound anomalies, we provide a generation algorithm that allows users to generate compound anomalies data of any possible combinations with existing collected data. We also provide a large dataset of both normal and anomalous monitoring data collected from various environments, facilitating the training of machine learning models and the evaluation of new algorithms for anomaly diagnosis.

The location of participants in Location-based CrowdSensing (LCS) represents important information for task completion. Tasks in areas with high concentration of participants (AHCP) can be completed quickly, whereas task completion is difficult in areas with sparse participants (ASP). Incentive mechanisms are necessary to motivate participants to move toward ASP. Previous studies have faced two main problems. First, most incentive mechanisms assume that participant motivation is not affected by external factors. Second, when participants fail to complete tasks, only the cost of the participant is considered the loss. However, reference effect from behavioral economics proves that participants are influenced by both internal and external factors. Furthermore, loss aversion studies have shown that participant evaluations of loss are more severe than simple costs. Therefore we propose an incentive mechanism based on behavioral economics (IBE) consisting of two schemes for participant selection (IBE-PS) and payment decisions (IBE-PD). Based on reference effect, IBE-PS is proposed to control the task selection and pricing of participants. Based on loss aversion, IBE-PD is proposed to encourage participants to complete tasks in ASP many times. Theoretical analysis and simulation results demonstrate that IBE can improve the task completion rate, the participant utility, and the platform welfare.

In mobile crowdsensing, many mobile devices are collectively used to complete complex sensing tasks. Most tasks require users to consume resources to ensure continuous performance over multiple periods of time. Therefore, it is important to incentivize enough users to continuously participate in the tasks. However, there are two issues with current incentive mechanisms. First, most studies are designed for maximizing the revenue of a single round of tasks rather than long-term incentives. Second, although some studies use historical data to design mechanisms for long-term operation, the law of diminishing marginal utility is not considered; thus, the actual performance is lower than expected. In this study, the concepts of capital deposit and intertemporal choice from behavioral economics are introduced to explain the principle of addiction, which is a representative long-term incentive. Consequently, an Addiction Incentive Mechanism (AIM) is proposed. It influences the utility and demand functions of users by accelerating the accumulation of capital deposits and promoting users to become addicted to cooperative behavior. It also mitigates the effect of diminishing marginal utility through intertemporal choice theory to maintain user engagement. Simulations demonstrate that AIM improves participation and repetition rates compared with the state-of-the-art mechanisms.

Second-order random walk is an important technique for graph analysis. Many applications use it to capture higher-order patterns in the graph, thus improving the model accuracy. However, the memory explosion problem of this technique hinders it from analyzing large graphs. When processing a billion-edge graph like Twitter, existing solutions (e.g., alias method) of the second-order random walk may take up 1796TB memory. Such high memory overhead comes from the memory-unaware strategies for node sampling across the graph. In this paper, to clearly study the efficiency of various node sampling methods in the context of second-order random walk, we design a cost model, and then propose a new node sampling method following the acceptance-rejection paradigm to achieve a better balance between memory and time cost. Further, to guarantee the efficiency of the second-order random walk within arbitrary memory budgets, we propose a memory-aware framework on the basis of the cost model. The framework applies a cost-based optimizer to assign desirable node sampling method for each node in the graph within a memory budget while minimizing the time cost. Finally, we provide general programming interfaces for users to benefit from the memory-aware framework easily. The empirical studies demonstrate that our memory-aware framework is robust with respect to memory and is able to achieve considerable efficiency by reducing 90% of the memory cost.

With the rapid growth of mobile traffic, Internet of Things requires a large number of access points (APs) to provide data offloading capabilities. Owing to their selfishness, most APs refuse to participate. Therefore, an effective incentive mechanism is necessary. There are two common problems with current incentive mechanisms: 1) they generally assume that APs make decision by calculating expected utility and 2) they also do not consider the time restrictions of the mechanisms themselves. Thus, this paper proposes an incentive mechanism comprising the anchoring effect and loss aversion on offloading (AELAO). The creative of AELAO is its use of anchoring effect (i.e., the influence of referencing a user's decision) and loss aversion (i.e., consequences become more intolerable when facing the same losses and benefits). In order to solve the first problem, this paper proposes the reference factor and price-break discounts factor based on the anchoring effect. The reference factor is used as the anchor value (i.e., reference point) to determine the number of APs participating in data offloading. The design of the price-break discounts factor is based on the reference factor. For the second problem, this paper presents two new concepts: 1) time pressure and 2) regret value. Based on APs' loss aversion, time pressure can encourage them to participate in data offloading as soon as possible within the given time limit. Theoretical analysis and simulation results show that AELAO can increase the amount of data offloading while improving the offloading value, the average utility, and the participation rate of APs.