ScoreShield: Differentially Private Release of Similarity Scores
A growing number of applications, such as biometrics and retrieval-augmented generation (RAG), rely on cosine similarity scores computed between vector embeddings of text, images, or audio. These systems return similarity scores through their APIs for ranking and verification. However, such releases can leak information about individual records and enable membership inference attacks. While differential privacy (DP) provides a principled metric for quantifying attack risks, naïve application of DP mechanisms---such as adding i.i.d. Gaussian noise to vector entries---leads to excessive distortion (i.e., low utility) at a given privacy constraint that scales poorly with the number of releas...
| Authors | Behrooz Razeghi, Parsa Rahimi |
|---|---|
| Primary category | cs.IR |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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