针对性差分隐私+元学习，提升推荐系统隐私与精度平衡
Meta-Learning and Targeted Differential Privacy to Improve the Accuracy-Privacy Trade-off in Recommendations
- 仅对敏感属性数据加噪，减少无谓扰动
- 元学习使模型更抗噪声，准确率提升明显
- 适合关注隐私保护的推荐系统研发者
在隐私保护推荐系统中，差分隐私（DP）噪声会降低推荐准确性，如何平衡是关键挑战。本文从数据和模型双层面应对：在数据层，仅对最可能暴露敏感属性（如性别、年龄）的用户数据施加差分隐私，称为针对性差分隐私（targeted DP），以减少不必要的扰动；在模型层，采用元学习提升模型对残余噪声的鲁棒性。实验表明，该方法在准确率和隐私风险上均优于标准差分隐私和全量差分隐私基线。结果证明，数据层选择性应用差分隐私结合模型层元学习，能有效平衡推荐准确性与用户隐私。
原文摘要 · Abstract (English)
Balancing differential privacy (DP) with recommendation accuracy is a key challenge in privacy-preserving recommender systems, since DP-noise degrades accuracy. We address this trade-off at both the data and model levels. At the data level, we apply DP only to the most stereotypical user data likely to reveal sensitive attributes, such as gender or age, to reduce unnecessary perturbation; we refer to this as targeted DP. At the model level, we use meta-learning to improve robustness to remaining DP-noise. This achieves a better trade-off between accuracy and privacy than standard approaches: Meta-learning improves accuracy and targeted DP leads to lower empirical privacy risk compared to uniformly applied DP and full DP baselines. Overall, our findings show that selectively applying DP at the data level together with meta-learning at the model level can effectively balance recommendation accuracy and user privacy.
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