arXiv:2606. 10314v1 Announce Type: new Abstract: Although the study of human trajectory anomalies is critical for advancing spatial data mining, empirical research remains severely hindered by a pervasive lack of ground-truth datasets.
By Yueyang Liu, Joon-Seok Kim, Andreas Z\"ufle
arXiv:2606. 27936v1 Announce Type: cross Abstract: The widespread collection of fine-grained location data by commercial data brokers creates a re-identification risk that is not widely recognised by the public.
By Oscar Thees, Roman M\"uller, Matthias Templ
The study investigates whether large language models (LLMs) can predict neighborhood-level human mobility without training data. Using anonymized Cuebiq data across four U.S. metropolitan areas, the authors compare zero‑shot LLM predictions to supervised baselines for various mobility outcomes and assess structural alignment with empirical trends. Results show supervised models outperform LLMs (average accuracy 0.580 vs. 0.435), with LLMs relying on coarse, stable priors that may exhibit biased treatment of protected-group predictors.
By Saad Mohammad Abrar, Eesha Kurella, Arnav Dadarya, Naman Awasthi, Kazi Tasnim Zinat, Vanessa Frias-Martinez
The paper introduces a new privacy vulnerability in diffusion language models (DLMs) called token‑level memorization asymmetry, derived from theoretical analysis of diffusion training dynamics. It proposes Q‑Skew, a quantile‑weighted skewness indicator, to perform membership inference on fine‑tuned DLMs, outperforming existing baselines across multiple datasets and models. Additionally, Q‑Skew can be used to extract personally identifiable information (PII), demonstrating a broader privacy attack surface.
By Shengfang Zhai, Leo Marchyok, Yuling Shi, Huanran Chen, Yinpeng Dong, Jiaheng Zhang, Sanghyun Hong
arXiv:2607. 08357v1 Announce Type: new Abstract: Human mobility data are essential for transportation optimization, urban planning, and resource allocation, yet real-world mobility data are costly to collect and difficult to share due to privacy concerns.
By Rongchao Xu, Lin Jiang, Dahai Yu, Ximiao Li, Taichi Liu, Desheng Zhang, Yuan Tian, Guang Wang
arXiv:2608. 05246v1 Announce Type: new Abstract: Existing personalized LLM benchmarks primarily rely on textual personas or isolated behavioral signals, providing limited evaluation of cross-domain behavioral personalization, where responses must be grounded in heterogeneous daily-life activities.
By Jiahao Zhang, Yongzhi Tong, Zelin Fu, Pengde Zhao, Yanmei Jiang, Jiang Feng, Min Yang