Multi-class Item Mining under Local Differential Privacy
Year: 2025
Type: preprint
Abstract: 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-$k$ item mining. Through theoretical analysis and extensive experiments, we verify the effectiveness and superiority of these methods.
Source: arXiv (Cornell University)
Authors: Mao, Yulian, Ye, Qingqing, Du, Rong, Wang, Qi, Huang, Kai, Hu, Haibo
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6892018853
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