Multi-class Item Mining under Local Differential Privacy

• URL: http://arxiv.org/abs/2504.13526v1
• Date: Fri, 18 Apr 2025 07:37:06 GMT
• Title: Multi-class Item Mining under Local Differential Privacy
• Authors: Yulian Mao, Qingqing Ye, Rong Du, Qi Wang, Kai Huang, Haibo Hu,
• Abstract summary: We propose frameworks for multi-class item mining, along with two mechanisms: validity to reduce the impact of invalid data, and correlated perturbation to preserve the relationship between labels and items.<n>We also apply these optimized methods to two multi-class item mining queries: frequency estimation and top-$k$ item mining.
• Score: 16.12696021148232
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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