Passenger demand forecasting helps optimize vehicle scheduling, thereby improving urban efficiency. Recently, attention-based methods have been used to adequately capture the dynamic nature of spatio-temporal data. However, existing methods that rely on heuristic masking strategies cannot fully adapt to the complex spatio-temporal correlations, hindering the model from focusing on the right context. These works also overlook the high-level correlations that exist in the real world. Effectively integrating these high-level correlations with the original correlations is crucial. To fill this gap, we propose the Aggregation Differential Transformer (ADFormer), which offers new insights to demand forecasting promotion. Specifically, we utilize Differential Attention to capture the original spatial correlations and achieve attention denoising. Meanwhile, we design distinct aggregation strategies based on the nature of space and time. Then, the original correlations are unified with the high-level correlations, enabling the model to capture holistic spatio-temporal relations. Experiments conducted on taxi and bike datasets confirm the effectiveness and efficiency of our model, demonstrating its practical value. The code is available at https://github.com/decisionintelligence/ADFormer.

Differential privacy (DP) is the de facto privacy standard against privacy leakage attacks, including many recently discovered ones against large language models (LLMs). However, we discovered that LLMs could reconstruct the altered/removed privacy from given DP-sanitized prompts. We propose two attacks (black-box and white-box) based on the accessibility to LLMs and show that LLMs could connect the pair of DPsanitized text and the corresponding private training data of LLMs by giving sample text pairs as instructions (in the blackbox attacks) or fine-tuning data (in the white-box attacks). To illustrate our findings, we conduct comprehensive experiments on modern LLMs (e.g., LLaMA-2, LLaMA-3, ChatGPT-3.5, ChatGPT-4, ChatGPT-4o, Claude-3, Claude-3.5, OPT, GPT-Neo, GPT-J, Gemma-2, and Pythia) using commonly used datasets (such as WikiMIA, Pile-CC, and Pile-Wiki) against both wordlevel and sentence-level DP. The experimental results show promising recovery rates, e.g., the black-box attacks against the word-level DP over WikiMIA dataset gave 72.18% on LLaMA2 (70B), 82.39% on LLaMA-3 (70B), 75.35% on Gemma-2, 91.2% on ChatGPT-4o, and 94.01% on Claude-3.5 (Sonnet). More urgently, this study indicates that these well-known LLMs have emerged as a new security risk for existing DP text sanitization approaches in the current environment.

Deep models excel in analyzing image data. However, recent studies on Black-Box Model Inversion (MI) Attacks against image models have revealed the potential to recover concealed (via specific masks) private training images using publicly available images from the same domain as the training data. This study introduces PriDM, a novel diffusion model-based MI attack, illustrating the increased vulnerability of image models. PriDM leverages range-null space decomposition to extract essential range-space information and incorporates it into the diffusion model's sampling process. This enables the recovery of private information from arbitrarily masked images relying solely on images only aligned with the same machine-learning tasks as the target model. To demonstrate PriDM's effectiveness, we conducted experiments with various adversary background knowledge, including different public dataset domains and image masks. Results show PriDM produces recovered images of significantly higher quality, approximately twice as good as existing methods. Moreover, in scenarios involving complex backgrounds, PriDM outperforms the state-of-the-art by approximately 70%. In specific background knowledge scenarios, such as compressed and blurred images, our method achieves an almost 100% success rate. Additionally, PriDM performs well with real-world background knowledge including individuals wearing masks and randomly masked face images, which are not considered by existing works.