CAT-LDP：本地差分隐私下的云边自适应分类体系

中文摘要 AI 辅助

推荐系统广泛应用于日常生活，但其直接收集和使用用户偏好数据可能导致隐私泄露。现有隐私保护推荐方法往往难以平衡用户隐私与推荐性能，在隐式反馈场景下该问题更为严重，数据稀疏性进一步加剧了隐私扰动造成的有用信号损失。为解决该问题，我们提出CAT-LDP，一种本地差分隐私（Local Differential Privacy，LDP）约束下的云本地协同推荐框架。CAT-LDP将分层分类树与自适应隐私预算分配策略相结合，在保护用户隐私的同时，保留用户活跃类别中的更多有用信号。具体而言，用户上传满足LDP的扰动类别画像，云端基于这些画像进行粗粒度候选生成，本地设备再利用未扰动的本地历史进行细粒度重排序。在Amazon Video Games数据集上的实验表明，在不同隐私预算下，CAT-LDP在HR@K和NDCG@K指标上始终优于其固定预算消融变体及代表性基线。结果显示，将类别空间建模与云本地任务解耦相结合，可有效降低长尾稀疏场景下的噪声放大，为隐式反馈推荐提供更好的隐私与效用平衡。

英文摘要

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.

发表机构

• Pittsburgh Institute Sichuan University（匹兹堡大学 四川大学）

机构由 AI 辅助整理，请以论文原文为准。