Location-Based Social Network (LBSN) check-in trajectory data are important for many practical applications like POI recommendation, advertising, and pandemic intervention. However, the high collection costs and ever-increasing privacy concerns prevent us from accessing large-scale LBSN trajectory data. The recent advances in synthetic data generation provide us with a new opportunity to achieve this, which utilizes generative AI to generate synthetic data that preserves the characteristics of real data while ensuring privacy protection. However, generating synthetic LBSN check-in trajectories remains challenging due to their spatially discrete, temporally irregular nature and the complex spatio-temporal patterns caused by sparse activities and uncertain human mobility. To address this challenge, we propose GeoGen, a two-stage coarse-to-fine framework for large-scale LBSN check-in trajectory generation. In the first stage, we reconstruct spatially continuous, temporally regular latent movement sequences from the original LBSN check-in trajectories and then design a Sparsity-aware Spatio-temporal Diffusion model (S^2TDiff) with an efficient denosing network to learn their underlying behavioral patterns. In the second stage, we design Coarse2FineNet, a Transformer-based Seq2Seq architecture equipped with a dynamic context fusion mechanism in the encoder and a multi-task hybrid-head decoder, which generates fine-grained LBSN trajectories based on coarse-grained latent movement sequences by modeling semantic relevance and behavioral uncertainty. Extensive experiments on four real-world datasets show that GeoGen excels state-of-the-art models for both fidelity and utility evaluation, e.g., it increases over 69% and 55% in distance and radius metrics on the FS-TKY dataset.

Large language models (LLMs) often exhibit undesirable behaviors, such as safety violations and hallucinations.Although inference-time steering offers a cost-effective way to adjust model behavior without updating its parameters, existing methods often fail to be simultaneously effective, utilitypreserving, and training-efficient due to their rigid, one-size-fits-all designs and limited adaptability.In this work, we present FineSteer, a novel steering framework that decomposes inference-time steering into two complementary stages-conditional steering and fine-grained vector synthesis-allowing finegrained control over when and how to steer internal representations.In the first stage, we introduce a Subspace-guided Conditional Steering (SCS) mechanism that preserves model utility by avoiding unnecessary steering.In the second stage, we propose a Mixture-of-Steering-Experts (MoSE) mechanism that captures the multimodal nature of desired steering behaviors and generates query-specific steering vectors for improved effectiveness.Through tailored designs in both SCS and MoSE, FineSteer maintains robust performance on general queries while adaptively optimizing steering vectors for targeted inputs in a training-efficient manner.Extensive experiments on safety and truthfulness benchmarks show that FineSteer outperforms the state-of-the-art methods in overall performance (e.g., A 7.6% improvement on TruthfulQA over Llama-3), achieving stronger steering performance with minimal utility loss.The code is available at https://github.com/YukinoAsuna/FineSteer.

Smart-home automations enable users to customize smart devices to react automatically to people, the environment, and more. For example, an automation might adjust the lights when people are at home or enable a garage door to open by voice command. While automations offer convenience and accessibility, they can also inadvertently expose users to security and privacy risks, such as leaking sensitive data or allowing untrusted parties to control users' devices. Prior work has shown that information flow analysis is a promising technique for identifying these kinds of risks, hypothesizing that the analysis would be yet more effective if it could differentiate between devices located in different places in the home. We tested this hypothesis by developing a tool that extends prior information flow analysis approaches to account for device location. We conducted an interview study with 22 participants to build a dataset of home automations to establish a ground truth to evaluate the tool. We found that incorporating device location leads to an improved analysis that identifies more of the vulnerabilities users care about (F1 score 0.74) compared to prior work (F1 score 0.29). Our results demonstrate the feasibility of incorporating device location into an information flow analysis and, perhaps more importantly, suggest additional ways to prevent security and privacy risks beyond controlling potentially unsafe information flows.

High-speed railways are widely used worldwide due to their high speeds, punctuality, and comfort. Autonomous operation and automatic obstacle avoidance technologies are the key to enhancing operational efficiency and ensuring passenger safety. By identifying the tracks, high-speed rail clearance can be effectively detected, which helps to determine the specific space through which the train travels. In this paper, a lightweight track line tracking model (Track${}^{\mathbf {2}}$Net) is proposed, which is based on keypoint alignment and track anchor identification. Firstly, the method mathematically analyses the shapes of tracks in non-turnout scenes and then implements a rapid track matching algorithm based on keypoint alignment. For complex track shapes that fail to match, the Complex Track Positioning Module (CTPM) is proposed. The module extracts features by pre-defining the track anchor lines and has better robustness in complex scenes. Additionally, a post-treatment track method is proposed for turnouts to further refine the detection of the track segment occupied by the train. In order to judge the accuracy of track detection more rigorously, improved evaluation metrics are proposed. On the track datasets of the train operation constructed in this paper, Track${}^{\mathbf {2}}$Net achieves an accuracy of 91.43% and processes images at a rate of 236 FPS. Experiments verify the effectiveness of the model in track line detection.

Fine-tuning pre-trained models for downstream tasks has led to a proliferation of open-sourced task-specific models. Recently, Model Merging (MM) has emerged as an effective approach to facilitate knowledge transfer among these independently fine-tuned models. MM directly combines multiple fine-tuned task-specific models into a merged model without additional training, and the resulting model shows enhanced capabilities in multiple tasks. Although MM provides great utility, it may come with security risks because an adversary can exploit MM to affect multiple downstream tasks. However, the security risks of MM have barely been studied. In this paper, we first find that MM, as a new learning paradigm, introduces unique challenges for existing backdoor attacks due to the merging process. To address these challenges, we introduce BadMerging, the first backdoor attack specifically designed for MM. Notably, BadMerging allows an adversary to compromise the entire merged model by contributing as few as one backdoored task-specific model. BadMerging comprises a two-stage attack mechanism and a novel feature-interpolation-based loss to enhance the robustness of embedded backdoors against the changes of different merging parameters. Considering that a merged model may incorporate tasks from different domains, BadMerging can jointly compromise the tasks provided by the adversary (on-task attack) and other contributors (off-task attack) and solve the corresponding unique challenges with novel attack designs. Extensive experiments show that BadMerging achieves remarkable attacks against various MM algorithms. Our ablation study demonstrates that the proposed attack designs can progressively contribute to the attack performance. Finally, we show that prior defense mechanisms fail to defend against our attacks, highlighting the need for more advanced defense. Our code is available at: https://github.com/jzhang538/BadMerging.

As location-based services (LBS) have grown in popularity, more human mobility data has been collected. The collected data can be used to build machine learning (ML) models for LBS to enhance their performance and improve overall experience for users. However, the convenience comes with the risk of privacy leakage since this type of data might contain sensitive information related to user identities, such as home/work locations. Prior work focuses on protecting mobility data privacy during transmission or prior to release, lacking the privacy risk evaluation of mobility data-based ML models. To better understand and quantify the privacy leakage in mobility data-based ML models, we design a privacy attack suite containing data extraction and membership inference attacks tailored for point-of-interest (POI) recommendation models, one of the most widely used mobility data-based ML models. These attacks in our attack suite assume different adversary knowledge and aim to extract different types of sensitive information from mobility data, providing a holistic privacy risk assessment for POI recommendation models. Our experimental evaluation using two real-world mobility datasets demonstrates that current POI recommendation models are vulnerable to our attacks. We also present unique findings to understand what types of mobility data are more susceptible to privacy attacks. Finally, we evaluate defenses against these attacks and highlight future directions and challenges.