Auditing Privacy in Multi-Tenant RAG under Account Collusion
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910278437830656 |
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| author | Burnat, Florian A. D. |
| author_facet | Burnat, Florian A. D. |
| contents | Multi-tenant RAG services often treat the account as the privacy boundary: each account receives an $(\varepsilon_{\text{acc}},δ_{\text{acc}})$-DP retrieval guarantee against the tenant index. We show that this framing understates leakage under same-index account collusion. For Gaussian noise-then-select retrieval, $k$ coordinated same-tenant accounts compose to joint leakage $Θ(\sqrt{k}\,\varepsilon_{\text{acc}})$, not $\varepsilon_{\text{acc}}$; we give a matching membership-inference attack and validate the predicted $\sqrt{k}$ AUC trend in scalar, top-$K$, trained-embedder, and production-scale HNSW settings. We then give a verifier-runnable audit protocol that attests noise-then-select retrieval and reports $(\textsf{PASS},\varepsilon_{\text{audit}})$ for coalitions up to a declared cap $k_{\max}$, without disclosing the index or changing the retrieval decision rule. The claim is retrieval-channel only: generation-channel leakage and adversarially robust coalition-size estimation are complementary audit predicates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_19847 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Auditing Privacy in Multi-Tenant RAG under Account Collusion Burnat, Florian A. D. Cryptography and Security Information Retrieval Machine Learning Multi-tenant RAG services often treat the account as the privacy boundary: each account receives an $(\varepsilon_{\text{acc}},δ_{\text{acc}})$-DP retrieval guarantee against the tenant index. We show that this framing understates leakage under same-index account collusion. For Gaussian noise-then-select retrieval, $k$ coordinated same-tenant accounts compose to joint leakage $Θ(\sqrt{k}\,\varepsilon_{\text{acc}})$, not $\varepsilon_{\text{acc}}$; we give a matching membership-inference attack and validate the predicted $\sqrt{k}$ AUC trend in scalar, top-$K$, trained-embedder, and production-scale HNSW settings. We then give a verifier-runnable audit protocol that attests noise-then-select retrieval and reports $(\textsf{PASS},\varepsilon_{\text{audit}})$ for coalitions up to a declared cap $k_{\max}$, without disclosing the index or changing the retrieval decision rule. The claim is retrieval-channel only: generation-channel leakage and adversarially robust coalition-size estimation are complementary audit predicates. |
| title | Auditing Privacy in Multi-Tenant RAG under Account Collusion |
| topic | Cryptography and Security Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2605.19847 |