In-context learning (ICL) enables large language models (LLMs) to adapt to new tasks by conditioning on demonstrations of question-answer pairs. Recently, ICL has been extended to allow tabular data to be used as demonstration examples by serializing individual records into natural language formats. However, it is well-known that LLMs can leak information from data it has been prompted on, and since tabular data often contain sensitive information, understanding how to protect tabular data used in ICL is a critical area of research. This work serves as an initial investigation into how differential privacy (DP) can be utilized to protect tabular data used in ICL. Specifically, we investigate the application of DP mechanisms for private tabular ICL via data privatization prior to serialization and prompting. We formulate two private ICL frameworks with provable privacy guarantees in both the local (LDP-TabICL) and global (GDP-TabICL) DP scenarios via injecting noise into individual records or group statistics, respectively. Our evaluations show that DP-based ICL can protect the privacy of the underlying tabular data while achieving comparable performance to non-LLM baselines, especially under high privacy regimes.

Graph Neural Networks have achieved tremendous success in modeling complex graph data in a variety of applications. However, there are limited studies investigating privacy protection in GNNs. In this work, we propose a learning framework that can provide local node privacy for users, while incurring low utility loss. We focus on a decentralized notion of Differential Privacy, namely Local Differential Privacy, and apply randomization mechanisms to perturb both feature and label data at the node level before they are collected by a server for model training. Specifically, we investigate the application of randomization mechanisms in high-dimensional feature settings and propose an LDP protocol with strict privacy guarantees. Based on frequency estimation in statistical analysis of randomized data, we develop reconstruction methods to approximate features and labels from perturbed data. We also formulate this learning framework to utilize frequency estimates of graph clusters to supervise the training procedure at a sub-graph level. Extensive experiments on real-world and semi-synthetic datasets demonstrate the validity of our proposed model.

Differential privacy, the current gold standard for data anonymization and protection, is commonly known to cause degraded utility, and exacerbate unfairness, for different demographic groups when it is used to train a private machine learning model. However, in contrast with this long-held perception, recent work has shown that local differential privacy, a variant of differential privacy where users perturb their data on their device before it is aggregated, can surprisingly lead to improved fairness measures without significantly affecting the utility of the underlying machine learning model. Motivated by this previous work, in this paper we further show that applying randomized response, a popular local differential privacy method, does not incur disparate impact on the private model’s accuracy for different demographic groups. Specifically, through conducting thorough empirical analysis in which we perform randomized response on the labels, the features, or on both the features and labels across multiple data modalities and model architectures, we empirically show that the absolute difference in utility loss for different demographic groups is negligible.

Collective classification utilizes network structure information via label propagation to improve prediction accuracy for node classification tasks. Because these models use information from previously labeled nodes which often contain historical bias, they may result in predictions that are biased w.r.t. the sensitive attributes of nodes such as race and gender. Throughout inference, this bias may even be amplified due to propagation especially for networks characterized by homophily. Despite past and ongoing research on fair classification, research to ensure fair collective classification s till remains unexplored. In this paper, we present a fair collective classification framework (denoted as FairCC) and formulate various heuristic methodologies, including node reweighting, threshold adjustment, and postprocessing, to achieve fair prediction. We also implement and test several naive methodologies for fair collective classification. Experiments on semi-synthetic datasets highlight the insufficiency of the naive methodologies and demonstrate the effectiveness of the proposed heuristics in significantly reducing prediction bias.