# Meta-Learning and Targeted Differential Privacy to Improve the Accuracy-Privacy Trade-off in Recommendations

arXiv: https://arxiv.org/abs/2604.26390 · HTML: https://mlsanity.com/paper/2604.26390/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 29 Apr 2026.

## Builds on (most cited first, 6 of 6)

- [MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation](https://mlsanity.com/paper/1908.00413/) (2019)
- [Meta Matrix Factorization for Federated Rating Predictions](https://mlsanity.com/paper/1910.10086/) (2019)
- [Data Scarcity in Recommendation Systems: A Survey](https://mlsanity.com/paper/2312.10073/) (2023)
- [The Unfairness of Popularity Bias in Book Recommendation](https://mlsanity.com/paper/2202.13446/) (2022)
- [Robustness of Meta Matrix Factorization Against Strict Privacy Constraints](https://mlsanity.com/paper/2101.06927/) (2021)
- [Making Alice Appear Like Bob: A Probabilistic Preference Obfuscation Method For Implicit Feedback Recommendation Models](https://mlsanity.com/paper/2406.11505/) (2024)

## Abstract (arXiv)

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.