Differentially Private Datastore Generation for Retrieval-Augmented Inference
It is crucial for modern on-device AI systems that rely on retrieval-augmented inference to release and share datastores without compromising individual privacy. This can be achieved using Differential Privacy (DP), which provides a formal guarantee that ensures individual contributions remain indistinguishable, even under adversarial analysis. In this paper, we introduce a hashing-based probability generation framework designed to enable the creation and release of differentially private datastores. Our approach employs locality-sensitive hashing (LSH) to efficiently partition high-dimensional data into buckets. We then add calibrated DP noise to the accumulated vote for each bucket, gen...
| Authors | Abdelrahman Abouelenein, Marwan Torki |
|---|---|
| Primary category | cs.CR |
| Published | 31 May 2026 |
| Last updated at source | 31 May 2026 |
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