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.

翻译：平衡差分隐私与推荐准确性是隐私保护推荐系统的核心挑战，因为差分隐私噪声会降低准确性。我们从数据层和模型层两个层面解决这一权衡问题。在数据层，我们仅对最可能暴露敏感属性（如性别或年龄）的典型用户数据应用差分隐私，以减少不必要的扰动——我们称之为目标差分隐私。在模型层，我们使用元学习来提高对剩余差分隐私噪声的鲁棒性。相较于标准方法，这实现了更优的精度-隐私权衡：与统一应用差分隐私和完全差分隐私的基线相比，元学习提升了准确性，而目标差分隐私则降低了经验隐私风险。总体而言，我们的研究表明，在数据层选择性应用差分隐私，同时结合模型层的元学习，能够有效平衡推荐准确性与用户隐私。