General frameworks such as FlowDroid, IccTA, P/Taint, Amandroid, and DroidSafe have significantly advanced the development of static analysis tools for Android security by providing fundamental facilities for them. However, while these frameworks have been instrumental in fostering progress, they often operate with inherent inefficiencies, such as redundant computations, reliance on separate tools, and unnecessary complexity, which are rarely scrutinized by the analysis tools that depend on them. This paper introduces PacDroid, a new static analysis framework for detecting security vulnerabilities in Android apps. PacDroid employs a simple yet effective pointer-analysis-centric approach that naturally manages alias information, interprocedural value propagation, and all Android features it supports (including ICC, lifecycles, and miscs), in a unified manner. Our extensive evaluation reveals that PacDroid not only outperforms state-of-the-art frameworks in achieving a superior trade-off between soundness and precision (F-measure) but also surpasses them in both analysis speed and robustness; moreover, PacDroid successfully identifies 77 real security vulnerability flows across 23 real-world Android apps that were missed by all other frameworks. With its ease of extension and provision of essential facilities, PacDroid is expected to serve as a foundational framework for various future analysis applications for Android.

Recent works try to combine clustering and contrastive learning for unsupervised out-of-distribution (OOD) detection, since these two schemes can exploit semantic information and bring in discriminative representation learning. However, most methods based on clustering and contrastive learning struggle with the problems of hard assignment and low-level clustering, i.e., they usually assign each sample to one single cluster and obtain clusters of similar low-level features, which can easily bring in numerous incorrect assignments and hinder the learning of semantic information. To address these problems, this paper proposes a novel framework for unsupervised OOD detection named Soft Cluster-aware Equivariant Contrastive Learning (SCECL). Different from previous works, SCECL devises two modules named Soft Cluster-aware Semantic Relationship Mining (SCSRM) and Contrastive Learning with Invariance and Equivariance (CLIE): SCSRM assigns each sample to multiple clusters with soft assignment weights and utilizes the soft assignment weights with semantic relationships to guide unsupervised contrastive learning for OOD detection, while CLIE introduces the equivariance principle as an additional inductive bias, encouraging the model to learn more discriminative semantic features to avoid low-level clustering. Extensive experimental results on various OOD detection benchmarks demonstrate that the proposed SCECL can effectively utilize semantic information for discriminative representation learning and achieve state-of-the-art performance.

Learning unsigned distance fields (UDF) directly from raw point clouds as the implicit representation for surface reconstruction is a promising learning-based method for reconstructing open surfaces and supervision-free attributes. In most UDF methods, Chamfer Distance (CD), the commonly used metric in 3D domains, is reckoned as the preferable loss function for training neural networks that predict UDFs. However, CD intrinsically suffers from deficiencies like the insensitivity to point density distribution and the inclination to be diverged by outliers, which may severely hamper the reconstruction performance. In this regard, we propose DM-UDF, a method that learns density-regulated and multi-view consistent UDFs by revising CD loss with the dynamic three-phase loss function. Specifically, we adopt a carefully designed CD derivative called Density-aware Chamfer Distance (DCD) for detecting different density distributions to alleviate the distribution imbalance problem in the reconstructed surfaces. Further, to generate surfaces with fine-grained local details, a differentiable rendering view loss is also introduced into the hybrid design of our loss function, measuring the fidelity of projected images under different camera poses to maintain multi-view consistency. We conducted surface reconstruction tasks on both synthetic and real scan datasets and experimental results show that DM-UDF achieves state-of-the-art performance. Code is available at dm-udf.

With the development of new-generation mobile communication technology, the Internet of Vehicles is playing an increasingly important role in people’s lives. However, the sensitive information contained in its “data fingerprinting” raises many privacy and security concerns. To better protect the location privacy of users on the Internet of Vehicles, this article proposes a collaborative service-based privacy protection scheme for the Internet of Vehicles. In this scheme, each requesting user first initiates a location service query by generating a pairing index of the location points set. Then a customized pairing result threshold is used to determine the collaborating users that can participate in the service response. And in the process, the location privacy of users is secured by location point generalization and encryption. In addition, redundant location service recommendations are eliminated by the proposed repeatability tests. Security analysis and experiments show that this scheme has good performance and anti-privacy leakages, forgery attacks, collusion attacks, etc.