Privacy Preserving Recommender Systems Balancing Personalization with Privacy

arXiv:2607.13328 · cs.CR, cs.AI, cs.LG · Submitted 2026-07-14 · Read on arXiv

Ranjeet K Jha, Venkata Suresh Gummadilli

cs.CR, cs.AI, cs.LG

Submitted: 2026-07-14

Code: https://github.com/pytorch/opacus

Project page: https://nijianmo.github.io/amazon/inde

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Personalization — 20× in this paper · explained in 2 episodes
• Differential Privacy — 15× in this paper · explained in 4 episodes
• Federated Learning — 9× in this paper · explained in 14 episodes
• Matrix Factorization — 8× in this paper
• Accuracy — 7× in this paper · explained in 1 episode
• Neural Collaborative Filtering — 7× in this paper
• Click-Through Rate — 5× in this paper
• Differential Privacy (DP) — 3× in this paper · explained in 7 episodes
• Adaptability — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• California Consumer Privacy Act — 1× in this paper
• California Privacy Rights Act — 1× in this paper
• Differentially Private — 1× in this paper · explained in 3 episodes
• Dynamic Adaptation — 1× in this paper · explained in 2 episodes
• Federated Learning (FL) — 1× in this paper · explained in 7 episodes
• General Data Protection Regulation — 1× in this paper
• Model Drift — 1× in this paper · explained in 4 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Steering — 1× in this paper · explained in 2 episodes
• World Wide Web — 1× in this paper

Sources

• Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
• Advances and Open Problems in Federated Learning

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