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AI Privacy Risks and Protection

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Auditing Privacy in Multi-Tenant RAG under Account Collusion
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Towards the Next Frontier of LLMs, Training on Private Data: A Cross-Domain Benchmark for Federated Fine-Tuning
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DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning
May 13, 2026Haaris Mehmood, Giorgos Tatsis, Dimitrios Alexopoulos +4Federated Learning Homomorphic Encryption
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May 13, 2026Mert Cihangiroglu, Alessandro Pegoraro, Phillip Rieger +2Privacy-Preserving Framework
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May 12, 2026Abtin Mahyar, Masoumeh Shafieinejad, Yuhan Liu +1Membership Inference Attacks Membership Inference
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May 11, 2026Tianyuan Zou, Liang Yue, Yang Liu +2Privacy-Utility Trade-Offs Streaming Video
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Differentially Private Auditing Under Strategic Response
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UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment
May 8, 2026Shih-Yu Lai, Hirozumi Yamaguchi, Shang-Tse Chen +2Federated Learning Heterogeneous Edge Devices
Less Random, More Private: What is the Optimal Subsampling Scheme for DP-SGD?
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Towards Differentially Private Reinforcement Learning with General Function Approximation
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Decentralized Conformal Novelty Detection via Quantized Model Exchange
May 7, 2026Kyle Loh, Yu XiangFalse Discovery Rate Privacy-Preserving Framework
MAGIQ: A Post-Quantum Multi-Agentic AI Governance System with Provable Security
May 7, 2026Sepideh Avizheh, Tushin Mallick, Alina Oprea +2Post-Quantum Cryptography Diffie-Hellman

May 19, 2026 · cs.CRJ/K move · Enter open · S save

Auditing Privacy in Multi-Tenant RAG under Account Collusion

Florian A. D. Burnat

University of Bath, Bath, UK.

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.

Efficient Privacy Loss Algorithm Auditing Membership Inference Attacks AI Privacy Risks and Protection Algorithmic Collusion

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