Large language models (LLMs) can leak sensitive training data through memorization and membership inference attacks. Prior work has primarily focused on strong adversarial assumptions, including attacker access to entire samples or long, ordered prefixes, leaving open the question of how vulnerable LLMs are when adversaries have only partial, unordered sample information. For example, if an attacker knows a patient has "hypertension," under what conditions can they query a model fine-tuned on patient data to learn the patient also has "osteoarthritis?" In this paper, we introduce a more general threat model under this weaker assumption and show that fine-tuned LLMs are susceptible to these fragment-specific extraction attacks. To systematically investigate these attacks, we propose two data-blind methods: (1) a likelihood ratio attack inspired by methods from membership inference, and (2) a novel approach, PRISM, which regularizes the ratio by leveraging an external prior. Using examples from medical and legal settings, we show that both methods are competitive with a data-aware baseline classifier that assumes access to labeled in-distribution data, underscoring their robustness.

Equitable urban transportation applications require high-fidelity digital representations of the built environment (streets, crossings, curb ramps and more). Direct inspections and manual annotations are costly at scale, while conventional machine learning methods require substantial annotated training data for adequate performance. This study explores vision language models as a tool for annotating diverse urban features from satellite images, reducing the dependence on human annotation. Although these models excel at describing common objects in human-centric images, their training sets may lack signals for esoteric built environment features, making their performance uncertain. We demonstrate a proof-of-concept using a vision language model and a visual prompting strategy that considers segmented image elements. Experiments on two urban features --- stop lines and raised tables --- show that while zero-shot prompting rarely works, the segmentation and visual prompting strategies achieve nearly 40% intersection-over-union accuracy. We describe how these results motivate further research in automatic annotation of the built environment to improve equity, accessibility, and safety at scale and in diverse environments.

Open data is frequently released spatially aggregated, usually to comply with privacy policies. However, coarse heterogeneous aggregations complicate learning and integration for downstream AI/ML systems. In this work, we consider models to disaggregate spatio-temporal data from a low-resolution, irregular partition (e.g., census tract) to a high-resolution, irregular partition (e.g., city block). We propose an overarching model named the Structurally-Aware Recurrent Network (SARN), which integrates structurally-aware spatial attention (SASA) layers into the Gated Recurrent Unit (GRU) model. The spatial attention layers capture spatial interactions among regions, while the gated recurrent module captures the temporal dependencies. Each SASA layer calculates both global and structural attention --- global attention facilitates comprehensive interactions between different geographic levels, while structural attention leverages the containment relationship between different geographic levels (e.g., a city block being wholly contained within a census tract) to ensure coherent and consistent results. For scenarios with limited historical training data, we explore transfer learning and show that a model pre-trained on one city variable can be fine-tuned for another city variable using only a few hundred samples. Evaluating these techniques on two mobility datasets, we find that on both datasets, SARN significantly outperforms other neural models (5.0% and 1.2%) and typical heuristic methods (40% and 14%), enabling us to generate realistic, high-quality fine-grained data for downstream applications.