CAT-LDP: Cloud-edge Adaptive Taxonomy under Local Differential Privacy

2026-09-04

AI Summary

为解决推荐系统中用户隐私与推荐性能的平衡问题，提出CAT-LDP框架，通过本地差分隐私约束下的云-边缘协作和自适应隐私预算分配策略来保护隐私同时提高推荐效果。

Abstract

Recommender systems are widely used in daily life, but their direct collection and use of user preference data can also lead to privacy leakage. Existing privacy-preserving recommendation methods often find it hard to balance user privacy and recommendation performance. This problem is more serious in implicit-feedback settings, where data sparsity further increases the loss of useful signals caused by privacy perturbation. To solve this problem, we propose CAT-LDP, a cloud-local collaborative recommendation framework under local differential privacy constraints. CAT-LDP combines a hierarchical taxonomy tree with an adaptive privacy budget allocation strategy to keep more useful signals in users' active categories while protecting user privacy. Specifically, users upload perturbed category profiles that satisfy LDP. Based on these profiles, the cloud performs coarse-grained candidate generation, and the local device then carries out fine-grained reranking by using unperturbed local history. Experiments on the Amazon Video Games dataset show that CAT-LDP consistently outperforms its fixed-budget ablation variant and representative baselines on HR@K and NDCG@K under different privacy budgets. The results show that combining category-space modeling with cloud-local task decoupling can effectively reduce noise amplification in long-tail sparse settings and provide a better balance between privacy and utility for implicit-feedback recommendation.

Problem

• Recommender Systems
• Privacy Leakage
• Local Differential Privacy
• Implicit Feedback
• Data Sparsity

Innovation

• local differential privacy
• adaptive privacy budget allocation
• cloud-edge collaboration
• hierarchical taxonomy tree