Given a table T in a database and a question Q in natural language, the table question answering (TQA) task aims to return an accurate answer to Q based on the content of T. Recent state-of-the-art solutions leverage large language models (LLMs) to obtain high-quality answers. However, most rely on proprietary, large-scale LLMs with costly API access, posing a significant financial barrier. This paper instead focuses on TQA with smaller, open-weight LLMs that can run on a desktop or laptop. This setting is challenging, as such LLMs typically have weaker capabilities than large proprietary models, leading to substantial performance degradation with existing methods. We observe that a key reason for this degradation is that prior approaches often require the LLM to solve a highly sophisticated task using long, complex prompts, which exceed the capabilities of small open-weight LLMs. Motivated by this observation, we present Orchestra, a multi-agent approach that unlocks the potential of accessible LLMs for high-quality, cost-effective TQA. Orchestra coordinates a group of LLM agents, each responsible for a relatively simple task, through a structured, layered workflow to solve complex TQA problems -- akin to an orchestra. By reducing the prompt complexity faced by each agent, Orchestra significantly improves output reliability. We implement Orchestra on top of AgentScope, an open-source multi-agent framework, and evaluate it on multiple TQA benchmarks using a wide range of open-weight LLMs. Experimental results show that Orchestra achieves strong performance even with small- to medium-sized models. For example, with Qwen2.5-14B, Orchestra reaches 72.1% accuracy on WikiTQ, approaching the best prior result of 75.3% achieved with GPT-4; with larger Qwen, Llama, or DeepSeek models, Orchestra outperforms all prior methods and establishes new state-of-the-art results across all benchmarks.

Causal fairness in databases is crucial to preventing biased and inaccurate outcomes in downstream tasks. While most prior work assumes a known causal model, recent efforts relax this assumption by enforcing additional constraints. However, these approaches often fail to capture broader attribute relationships that are critical to maintaining utility. This raises a fundamental question: Can we harness the benefits of causal reasoning to design efficient and effective fairness solutions without relying on strong assumptions about the underlying causal model? In this paper, we seek to answer this question by introducing CausalPre, a scalable and effective causality-guided data pre-processing framework that guarantees justifiable fairness, a strong causal notion of fairness. CausalPre extracts causally fair relationships by reformulating the originally complex and computationally infeasible extraction task into a tailored distribution estimation problem. To ensure scalability, CausalPre adopts a carefully crafted variant of low-dimensional marginal factorization to approximate the joint distribution, complemented by a heuristic algorithm that efficiently tackles the associated computational challenge. Extensive experiments on benchmark datasets demonstrate that CausalPre is both effective and scalable, challenging the conventional belief that achieving causal fairness requires trading off relationship coverage for relaxed model assumptions.

Federated learning (FL) is a popular distributed machine learning (ML) framework in which multiple parties share their model parameters instead of the raw training datasets to construct a global model in a privacy-preserving manner. However, existing FL solutions mainly focus on protecting the privacy of individual training records by incorporating differential privacy (DP), while overlooking the protection of the distribution information of training datasets, despite the fact that data distribution is also regarded as highly sensitive in high-stakes applications.In this paper, we propose the first privacy-preserving stochastic gradient descent (SGD) algorithm for protecting label distribution in FL. To establish a formal privacy guarantee, we formalize a privacy notion, dubbed (m,γ,ξ)-label distributional privacy, to quantify label distributional privacy leakage. Subsequently, we design the label distribution perturbation mechanism (LDPM) that carefully incorporates randomness into the SGD algorithm to achieve (m,γ,ξ)-label distributional privacy for all one-vs-all classification models. LDPM is easy to implement and provides non-trivial privacy guarantees, making it a suitable drop-in replacement for existing FL local model training algorithms. Notably, we demonstrate that LDPM also ensures DP, indicating that LDPM offers both individual and label distributional privacy guarantees. Extensive experiments on six benchmark datasets validate the effectiveness of LDPM.

Given a group size m and a sensitive dataset D , group privacy (GP) releases information about D (e.g., weights of a neural network trained on D) with the guarantee that the adversary cannot infer with high confidence whether the underlying data is D or a neighboring dataset D ′ that differs from D by m records. GP generalizes the well-established notion of differential privacy (DP) for protecting individuals' privacy; in particular, when m = 1, GP reduces to DP. Compared to DP, GP is capable of protecting the sensitive aggregate information of a group of up to m individuals, e.g., the average annual income among members of a yacht club. Despite its longstanding presence in the research literature and its promising applications, GP is often treated as an afterthought, with most approaches first developing a differential privacy (DP) mechanism and then using a generic conversion to adapt it for GP, treating the DP solution as a black box. As we point out in the paper, this methodology is suboptimal when the underlying DP solution involves subsampling, e.g., in the classic DP-SGD method for training deep learning models. In this case, the DP-to-GP conversion is overly pessimistic in its analysis, leading to high error and low utility in the published results under GP. Motivated by this, we propose a novel analysis framework that provides tight privacy accounting for subsampled GP mechanisms. Instead of converting a black-box DP mechanism to GP, our solution carefully analyzes and utilizes the inherent randomness in subsampled mechanisms, leading to a substantially improved bound on the privacy loss with respect to GP. The proposed solution applies to a wide variety of foundational mechanisms with subsampling. Extensive experiments with real datasets demonstrate that compared to the baseline convert-from-blackbox-DP approach, our GP mechanisms achieve noise reductions of over an order of magnitude in several practical settings, including deep neural network training.

Diffusion models have recently gained significant attention in both academia and industry due to their impressive generative performance in terms of both sampling quality and distribution coverage. Accordingly, proposals are made for sharing pre-trained diffusion models across different organizations, as a way of improving data utilization while enhancing privacy protection by avoiding sharing private data directly. However, the potential risks associated with such an approach have not been comprehensively examined. In this paper, we take an adversarial perspective to investigate the potential privacy and fairness risks associated with the sharing of diffusion models. Specifically, we investigate the circumstances in which one party (the sharer) trains a diffusion model using private data and provides another party (the receiver) black-box access to the pre-trained model for downstream tasks. We demonstrate that the sharer can execute fairness poisoning attacks to undermine the receiver’s downstream models by manipulating the training data distribution of the diffusion model. Meanwhile, the receiver can perform property inference attacks to reveal the distribution of sensitive features in the sharer’s dataset. Our experiments conducted on real-world datasets demonstrate remarkable attack performance on different types of diffusion models, which highlights the critical importance of robust data auditing and privacy protection protocols in pertinent applications.

Federated learning (FL) has emerged as a promising privacy-preserving machine learning paradigm, enabling data owners to collaboratively train a joint model by sharing model parameters instead of private training data. However, recent studies reveal the privacy risks in FL by inferring private training data from model parameters. Therefore, differential privacy (DP) is incorporated into FL to safeguard training data. Nevertheless, DP does not provide a strong theoretical guarantee for protecting data distribution, which is also highly sensitive in thecross-siloFL scenarios as it may reflect the business secrets of data owners. In this paper, we develop two attack methods to investigate the potential risks of data distribution leakage in differentially private cross-silo FL. We highlight that an honest-but-curious server can successfully infer both the feature and label distributions of each party's training data without any background knowledge. Specifically, the first attack applies when models are differentiable, while the second attack caters to non-differentiable classification models. Extensive experiments on six benchmark datasets validate the effectiveness of the proposed attacks. The results demonstrate that the state-of-the-art DP-SGD algorithm is still vulnerable to the inference attack on data distribution, emphasizing the necessity of designing more advanced privacy-preserving FL frameworks.

As a solution concept in cooperative game theory, Shapley value is highly recognized in model interpretability studies and widely adopted by the leading Machine Learning as a Service (MLaaS) providers, such as Google, Microsoft, and IBM. However, as the Shapley value-based model interpretability methods have been thoroughly studied, few researchers consider the privacy risks incurred by Shapley values, despite that interpretability and privacy are two foundations of machine learning (ML) models.