Sharing personal photos on social media exposes users to unauthorized identity recognition and unconsented model training, raising severe privacy and copyright concerns. Existing methods typically focus on either privacy protection, which misleads recognition models to prevent unauthorized automated recognition, or tracing forensics, which embeds traceable patterns for ownership verification. However, they fail to achieve both simultaneously. The core challenge is to jointly achieve privacy protection and tracing forensics within a single perturbation, since the two objectives rely on different feature behaviors and naive combinations are ineffective in practice. In this work, we propose ATP (Adversarial Tracing Perturbation), a novel perturbation generation method that activates robustness-aware feature channels to balance privacy and traceability. ATP leverages non-robust channel activation to mislead recognition models for privacy protection, while robust channel activation embeds traceable patterns for reliable tracing forensics. Extensive experiments on image classification and face recognition show that ATP achieves strong dual protection, improving overall dual-protection performance by bm 3.54× over the baselines while remaining effective under adaptive attacks, thereby demonstrating strong robustness and practical applicability. © 2026 IEEE.

Microcontroller units (MCUs) are widely used in embedded devices due to their low power consumption and cost-effectiveness. MCU firmware controls these devices and is vital to the security of embedded systems. However, performing dynamic security analyses for MCU firmware has remained challenging due to the lack of usable execution environments -- existing dynamic analyses cannot run on physical devices (e.g., insufficient computational resources), while building emulators is costly due to the massive amount of heterogeneous hardware, especially peripherals. Recent advances in automated peripheral emulation have made MCU emulation more scalable. However, these efforts only support limited peripherals and are hard to extend because they require ad-hoc adaptations.

The Tor network, while offering anonymity through traffic routing across volunteer-operated nodes, remains vulnerable to attacks that aim to deanonymize users by correlating traffic patterns between colluded entry and exit nodes in circuits. This paper presents a novel approach for detecting anomalous circuits in the Tor network, and for the first time provides a more comprehensive identification of potential malicious accomplice nodes in Tor by taking roles of nodes in anomalous circuits into consideration. Our method strategically utilizes modified middle nodes to capture traffic data, followed by a novel circuit classification based on traffic patterns to pinpoint concerned circuits. Two kinds of anomalies are identified: routing anomalies and usage anomalies, that respectively represent the anomalies with explicit or implicit violation of Tor's circuit construction guidelines. This leads to a successful revealing of totally 1,960 anomalous nodes in Tor. Furthermore, we apply clustering analysis with considering corresponding anomalous circuits and other key characteristics to the detected anomalous nodes, revealing potential hidden organizations behind these nodes that can threaten the network's security. Our findings highlight the necessity for the Tor project to adopt targeted mitigation strategies to enhance overall network security and privacy.

Vision-Language Pre-training (VLP) models have achieved remarkable success in practice, while easily being misled by adversarial attack. Though harmful, adversarial attacks are valuable in revealing the blind-spots of VLP models and promoting their robustness. However, existing adversarial attacking studies pay insufficient attention to the key roles of different modality-correlated features, leading to unsatisfactory transferable attacking performance. To tackle this issue, we propose the Transferable MultiModal (TMM) attack framework, which tailors both the modality consistency and modality discrepancy features. To promote transferability, we propose the attention-directed feature perturbation to disturb the modality-consistency features in critical attention regions. In light of the commonly employed cross-attention can represent the consistent features among diverse models, it is more possible to mislead the similar model perception for activating stronger transferability. For improving attacking ability, we proposed the orthogonal-guided feature heterogenization to guide the adversarial perturbation to contain more modality-discrepancy features in the encoded embeddings. Since VLP models rely more on aligned features among different modalities during decision-making, increasing the modality-discrepant could confuse the learned representation for better attacking ability. Extensive experiments under diverse settings demonstrate that the proposed TMM outperforms the comparisons by large margins, i.e., 20.47% improvements in transferable attacking ability on average. Moreover, we highlight that our TMM also shows outstanding attacking performance on large models, such as MiniGPT-4, Otter, etc.

The development of home internet of things (H-IoT) devices brings convenience but poses significant privacy and security risks. Existing research minimizes data uploaded to the cloud but fails to process data locally, resulting in a trade-off between privacy and functionality. In this paper, we propose a privacy-preserving method that identifies and processes sensitive data sent from H-IoT devices at the edge side, ensuring functionality while preserving privacy. Our method applies different identification strategies to packets with different features, making it applicable to most H-IoT devices and scenarios. We validate our approach through experiments on a prototype system that monitors multiple cameras, demonstrating its effectiveness in preserving privacy while maintaining functionality.

IFTTT is one of the most popular Trigger-Action Programming platforms. The rules generated in IFTTT are named IoT Applets. Despite the powerful programming interface provided by IFTTT, establishing an Applet requires technical skills and is not convenient enough for most users. To address this problem, we propose a gesture based programming method to help end users establish and manage IoT Applets in a convenient way. It requires employment of an RGB-D camera, and recognizes users’ pointing rays and hand actions. The obtained information is interpreted to certain devices and device events for Applet management. An experiment involving 20 participants validates the performance of our proposed method.

IFTTT is a programming framework for Applets (i.e., user customized policies with a "trigger-action" syntax), and is the most popular Home Internet-of-Things (H-IoT) platform. The execution of an Applet prompted by a device operation suffers from a long delay, since IFTTT has to periodically reads the states of the device to determine whether the trigger is satisfied, with an interval of up to 5min for professionals and 60min for normal users. Although IFTTT sets up a flexible polling interval based on the past several times an Applet has run, the delay is still around 2min even for frequently executed Applets. This paper proposes a novel trigger notification mechanism "RTX-IFTTT" to implement real-time execution of Applets. The mechanism does not require any changes to the current IFTTT framework or the H-IoT devices, but only requires an H-IoT edge node (e.g., router) to identify the device events (e.g., turning on/off) and notify IFTTT to perform the action of an Applet when an identified event is the trigger of that Applet. The experimental results show that the averaged Applet execution delay for RTX-IFTTT is only about 2sec.

With the development of smart mobile devices, location privacy has gained attention from both academia and industry. In recent years, a variety of location privacy definitions from different perspectives have been proposed to quantify location privacy and compare location privacy protection mechanisms (LPPMs). These definitions, however, have some drawbacks. In this paper, we propose a location privacy metric for discrete location information which improves the quantification of distance between the prior and posterior distribution of an adversary who may hold background knowledge in differential privacy. Furthermore, we develop a non-convex optimization problem and construct a near-optimal mechanism. We evaluate our proposed metric by comparing it to the state-of-the-art definitions including Shokri's incorrectness, Andrés's geo-indistinguishability and Dong's DPLO. We also evaluate our proposed mechanism with the optimal mechanisms based on the afore mentioned existing definitions. We make experiments on both simulation and realworld dataset, and the results show that our proposed metric and mechanism have the ascendant position.

Analyzing the popularity of microblogging services is of great significance in various applications. The number of posts provides novel insights into the popularity of microblogging services, and is critical to learn about the frequency of use. Existing approaches analyze this parameter by observing posts published by a large number of users, which may lead to underestimate the value since the sampled user may stop to use the service during the observing process. In this paper, we propose a novel method to estimate the number of posts in microblogging services. The basic idea behind this method is to make use of a common API provided by microblogging services, i.e., the public_timeline API. Posts sampled by this API may duplicate among multiple invocations, so the capture-recapture model can be used to estimate the total number of posts. Based on the traditional capture-recapture model, we propose an improved model to address challenges on low sampling probability and unequal sampling probability. We validate the proposed method using a real life Sina Weibo dataset, and the experimental results demonstrate the effectiveness and accuracy of our proposed method.

Green wireless local area networks (WLANs) have captured the interests of academia and industry recently, because they save energy by scheduling an access point (AP) on/off according to traffic demands. However, it is very challenging to determine user association in a green WLAN while simultaneously considering several other factors, such as avoiding AP congestion and user migration constraints. Here, we study the energy‐efficient user association with congestion avoidance and migration constraint (EACM). First, we formulate the EACM problem as an integer linear programming (ILP) model, to minimize APs’ overall energy consumption within a time interval while satisfying the following constraints: traffic demand, AP utilization threshold, and maximum number of demand node (DN) migrations allowed. Then, we propose an efficient migration‐constrained user reassociation algorithm, consisting of two steps. The first step removeskAP‐DN associations to eliminate AP congestion and turn off as many idle APs as possible. The second step reassociates thesekDNs according to an energy efficiency strategy. Finally, we perform simulation experiments that validate our algorithm’s effectiveness and efficiency.

The development of the Internet of Things has accelerated research in the indoor location fingerprinting technique, which provides value-added localization services for existing WLAN infrastructures without the need for any specialized hardware. The deployment of a fingerprinting based localization system requires an extremely large amount of measurements on received signal strength information to generate a location fingerprint database. Nonetheless, this requirement can rarely be satisfied in most indoor environments. In this paper, we target one but common situation when the collected measurements on received signal strength information are insufficient, and show limitations of existing location fingerprinting methods in dealing with inadequate location fingerprints. We also introduce a novel method to reduce noise in measuring the received signal strength based on the maximum likelihood estimation, and compute locations from inadequate location fingerprints by using the stochastic gradient descent algorithm. Our experiment results show that our proposed method can achieve better localization performance even when only a small quantity of RSS measurements is available. Especially when the number of observations at each location is small, our proposed method has evident superiority in localization accuracy.

Using mobile devices, people can easily obtain their location information, and access a wide range of location based services (LBSs). Many existing LBSs rely in accurate, continuous, and real-time streams of location information to provide quality of service guarantees. In this case, even if an user accesses LBSs anonymously, the identity of the user can still be revealed by analyzing the mobility trace. To protect user privacy, existing work sacrifice the quality of LBSs by degrading spatial and temporal accuracy. To achieve a better tradeoff between user privacy and the quality of service, we present a novel approach, Complete Bipartite Anonymity (CBA), to confuse the paths of nearby users by connecting different users' real traces with fake ones. CBA protects user privacy as users become indistinguishable after their paths are confused, the quality of service of LBSs is also guaranteed since users are able to report their accurate locations. We evaluate CBA by comparing the system and privacy performance with existing techniques such as Path Confusion or Query Obfuscation using a real-world data set, the results show that our scheme increases the chance for a user joining an anonymity group by 10 times in low user density areas, and reduces the resources consumed by about 90% for achieving the same anonymity degree.

The emerging participatory sensing applications have brought a privacy risk where users expose their location information. Most of the existing solutions preserve location privacy by generalizing a precise user location to a coarse-grained location, and hence they cannot be applied in those applications requiring fine-grained location information. To address this issue, in this paper we propose a novel method to preserve location privacy by anonymizing coarse-grained locations and retaining fine-grained locations using Attribute Based Encryption (ABE). In addition, we do not assume the service provider is an trustworthy entity, making our solution more feasible to practical applications. We present and analyze our security model, and evaluate the performance and scalability of our system.