Item mining, a fundamental task for collecting statistical data from users, has raised increasing privacy concerns. To address these concerns, local differential privacy (LDP) was proposed as a privacy-preserving technique. Existing LDP item mining mechanisms primarily concentrate on global statistics, i.e., those from the entire dataset. Nevertheless, they fall short of user-tailored tasks such as personalized recommendations, whereas classwise statistics can improve task accuracy with fine-grained information. Meanwhile, the introduction of class labels brings new challenges. Label perturbation may result in invalid items for aggregation. To this end, we propose frameworks for multi-class item mining, along with two mechanisms: validity perturbation to reduce the impact of invalid data, and correlated perturbation to preserve the relationship between labels and items. We also apply these optimized methods to two multi-class item mining queries: frequency estimation and top- item mining. Through theoretical analysis and extensive experiments, we verify the effectiveness and superiority of these methods.
View on arXiv@article{mao2025_2504.13526,
title={ Multi-class Item Mining under Local Differential Privacy },
author={ Yulian Mao and Qingqing Ye and Rong Du and Qi Wang and Kai Huang and Haibo Hu },
journal={arXiv preprint arXiv:2504.13526},
year={ 2025 }
}