arXiv subjects

Xueling Zhang

Publications and source records attributed to Xueling Zhang.

5 recordsLinked to original sources

arXiv2026

Beyond GDPR: Examining Disclosure Gaps in Mobile AR Privacy Policies under U.S. State Privacy Laws

Mobile Augmented Reality (MAR) apps can collect and process highly sensitive data such as spatial maps and biometrics, yet their privacy policies remain largely understudied. Prior audits of app privacy policies have typically focused on a single legal framework, such as the GDPR. Meanwhile, 20 U.S. states have comprehensive privacy laws in effect, creating a fragmented and rapidly evolving set of privacy policy obligations. To date, no study has systematically audited privacy policies against this emerging body of state-level legislation. In this paper, we present the first large-scale audit of MAR privacy policies under U.S. state privacy laws. We construct a dataset covering the MAR ecosystem, including 8,013 Google Play MAR app metadata records worldwide, and a U.S.-based subset with 6,620 APKs and 6,426 privacy policy files. We further derive an auditable disclosure taxonomy with 5 baseline requirements, 10 triggered requirements, and 4 logic chains, and build a validated four-stage automated pipeline that produces traceable, evidence-grounded disclosure judgments. Our audit reveals widespread disclosure gaps: 44.62\% of audited policies exhibit severe disclosure omissions, with each missing more than eight requirements, and four privacy-policy requirements have violation rates above 90\%. These findings suggest that MAR privacy disclosures are not keeping pace with the growing complexity of U.S. state privacy regulation. We release our dataset, taxonomy, and auditing pipeline to support future research on scalable privacy compliance auditing.

cs.CR↗

arXiv2026

Is Your Private Information Logged? An Empirical Study on Android App Logs

With the rapid growth of mobile apps, users' concerns about their privacy have become increasingly prominent. Android app logs serve as crucial computer resources, aiding developers in debugging and monitoring the status of Android apps, while also containing a wealth of software system information. Previous studies have acknowledged privacy leaks in software logs and Android apps as significant issues without providing a comprehensive view of the privacy leaks in Android app logs. In this study, we build a comprehensive dataset of Android app logs and conduct an empirical study to analyze the status and severity of privacy leaks in Android app logs. Our study comprises three aspects: (1) Understanding real-world developers' concerns regarding privacy issues related to software logs; (2) Studying privacy leaks in the Android app logs; (3) Investigating the characteristics of privacy-leaking Android app logs and analyzing the reasons behind them. Our study reveals five different categories of concerns from real-world developers regarding privacy issues related to software logs and the prevalence of privacy leaks in Android app logs, with the majority stemming from developers' unawareness of such leaks. Additionally, our study provides developers with suggestions to safeguard their privacy from being logged.

cs.SE↗

arXiv2026

TokenSeek: Memory Efficient Fine Tuning via Instance-Aware Token Ditching

Fine tuning has been regarded as a de facto approach for adapting large language models (LLMs) to downstream tasks, but the high training memory consumption inherited from LLMs makes this process inefficient. Among existing memory efficient approaches, activation-related optimization has proven particularly effective, as activations consistently dominate overall memory consumption. Although prior arts offer various activation optimization strategies, their data-agnostic nature ultimately results in ineffective and unstable fine tuning. In this paper, we propose TokenSeek, a universal plugin solution for various transformer-based models through instance-aware token seeking and ditching, achieving significant fine-tuning memory savings (e.g., requiring only 14.8% of the memory on Llama3.2 1B) with on-par or even better performance. Furthermore, our interpretable token seeking process reveals the underlying reasons for its effectiveness, offering valuable insights for future research on token efficiency. Homepage: https://runjia.tech/iclr_tokenseek/

cs.CL↗

arXiv2025

MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper

Considering deep neural networks as manifold mappers, the pretrain-then-fine-tune paradigm can be interpreted as a two-stage process: pretrain establishes a broad knowledge base, and fine-tune adjusts the model parameters to activate specific neural pathways to align with the target manifold. Although prior fine-tuning approaches demonstrate success, their rigid parameter space limits their ability to dynamically activate appropriate neural pathways, rendering them ill-equipped to adapt flexibly to the diverse and evolving data distributions. In light of this view, we propose a novel approach, Mixture of Expert Prompt Tuning (MEPT), as an effective and efficient manifold-mapping framework. MEPT leverages the Mixture of Experts architecture by integrating multiple prompt experts to adaptively learn diverse and non-stationary data distributions. Empirical evaluations demonstrate that MEPT outperforms several state-of-the-art parameter efficient baselines on SuperGLUE, achieving notable improvements in mean accuracy (e.g., 1.94%) while significantly reducing activated prompts by 79.25%. The effectiveness of MEPT is further supported by theoretical insights from manifold learning and validated through neural activation pathway visualization results. Our code is avaliable at https://runjia.tech/emnlp_mept/.

cs.LG↗

arXiv2024

ProMotion: Prototypes As Motion Learners

In this work, we introduce ProMotion, a unified prototypical framework engineered to model fundamental motion tasks. ProMotion offers a range of compelling attributes that set it apart from current task-specific paradigms. We adopt a prototypical perspective, establishing a unified paradigm that harmonizes disparate motion learning approaches. This novel paradigm streamlines the architectural design, enabling the simultaneous assimilation of diverse motion information. We capitalize on a dual mechanism involving the feature denoiser and the prototypical learner to decipher the intricacies of motion. This approach effectively circumvents the pitfalls of ambiguity in pixel-wise feature matching, significantly bolstering the robustness of motion representation. We demonstrate a profound degree of transferability across distinct motion patterns. This inherent versatility reverberates robustly across a comprehensive spectrum of both 2D and 3D downstream tasks. Empirical results demonstrate that ProMotion outperforms various well-known specialized architectures, achieving 0.54 and 0.054 Abs Rel error on the Sintel and KITTI depth datasets, 1.04 and 2.01 average endpoint error on the clean and final pass of Sintel flow benchmark, and 4.30 F1-all error on the KITTI flow benchmark. For its efficacy, we hope our work can catalyze a paradigm shift in universal models in computer vision.

cs.CV↗