arXiv Machine Learning By Abdelrahman Abouelenein, Marwan Torki

Differentially Private Datastore Generation for Retrieval-Augmented Inference

Read the original on arXiv Machine Learning →

arXiv:2606. 01413v1 Announce Type: cross Abstract: It is crucial for modern on-device AI systems that rely on retrieval-augmented inference to release and share datastores without compromising individual privacy.

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arXiv Machine Learning
Jun 30

Efficient Unlearning with Privacy Guarantees

arXiv:2507. 04771v2 Announce Type: replace-cross Abstract: Privacy protection laws, such as the GDPR, grant individuals the right to request the forgetting of their personal data not only from databases but also from machine learning (ML) models trained on them.

By Josep Domingo-Ferrer, Najeeb Jebreel, David S\'anchez
arXiv Machine Learning
Aug 27

Pointing the Way, Hiding the Destination: Practical Private Dense Retrieval at Scale

The paper presents a practical private dense retrieval system that uses learned deep hashing as a private filter to generate a short candidate list for each query. Encrypted reranking and oblivious key transfer protect the exact query and final selection, allowing the system to match full‑corpus retrieval quality with only 200‑500 candidates. Experiments on five zero‑shot corpora and the 2.68M‑passage NQ corpus show minimal latency overhead and strong privacy guarantees.

By Peichun Hua, Danyang Chen, Junan Zhang, Haifeng Sun, Jingyu Wang, Diwen Xue, Mingyu Li, Yunming Xiao