Evaluating Differential Privacy Against Membership Inference in Federated Learning: Insights from the NIST Genomics Red Team Challenge

• URL: http://arxiv.org/abs/2604.12737v2
• Date: Wed, 15 Apr 2026 10:00:01 GMT
• Title: Evaluating Differential Privacy Against Membership Inference in Federated Learning: Insights from the NIST Genomics Red Team Challenge
• Abstract summary: This paper presents an empirical evaluation of Differential Privacy (DP) as a defense mechanism against membership inference attacks (MIAs) in Federated Learning (FL)<n>We propose a stacking attack strategy that ensembles seven black-box estimators to train a meta-classifier on prediction probabilities and cross-entropy losses.<n>We evaluate this methodology against target models under three privacy configurations.
• Score: 0.20305676256390934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While Federated Learning (FL) mitigates direct data exposure, the resulting trained models remain susceptible to membership inference attacks (MIAs). This paper presents an empirical evaluation of Differential Privacy (DP) as a defense mechanism against MIAs in FL, leveraging the environment of the 2025 NIST Genomics Privacy-Preserving Federated Learning (PPFL) Red Teaming Event. To improve inference accuracy, we propose a stacking attack strategy that ensembles seven black-box estimators to train a meta-classifier on prediction probabilities and cross-entropy losses. We evaluate this methodology against target models under three privacy configurations: an unprotected convolutional neural network (CNN, $ε=\infty$), a low-privacy DP model ($ε=200$), and a high-privacy DP model ($ε=10$). The attack outperforms all baselines in the No DP and Low Privacy settings and, critically, maintains measurable membership leakage at $ε=200$ where a single-signal LiRA baseline collapses. Evaluated on an independent third-party benchmark, these results provide an empirical characterisation of how stacking-based inference degrades across calibrated DP tiers in FL.

Related papers

• Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data [55.84428098924793]
  We propose Constrained Uniform Top-K Sampling (CUTS), a parameter-free decoding strategy enforcing structure-preserving exploration.<n>We integrate this into Mixed-CUTS, a training framework synergizing exploitative and exploratory rollouts to amplify intra-group advantage variance.<n> Notably, Mixed-CUTS improves Pass@1 accuracy on the challenging AIME25 benchmark by up to 15.1% over standard GRPO.
  arXiv  Detail & Related papers  (2026-04-20T16:43:28Z)
• FL-PBM: Pre-Training Backdoor Mitigation for Federated Learning [15.911410230033816]
  Backdoor attacks pose a significant threat to the integrity and reliability of Artificial Intelligence (AI) models.<n>In this paper, we propose Pre-Training Backdoor Mitigation for Federated Learning.<n>It proactively filters poisoned data on the client side before model training in a federated learning environment.
  arXiv  Detail & Related papers  (2026-03-30T16:56:38Z)
• Differential Privacy: Gradient Leakage Attacks in Federated Learning Environments [0.6850683267295249]
  Federated Learning (FL) allows for the training of Machine Learning models in a collaborative manner without the need to share sensitive data.<n>It remains vulnerable to Gradient Leakage Attacks (GLAs), which can reveal private information from the shared model updates.<n>We investigate the effectiveness of Differential Privacy mechanisms as defenses against GLAs.
  arXiv  Detail & Related papers  (2025-10-27T23:33:21Z)
• On Evaluating the Poisoning Robustness of Federated Learning under Local Differential Privacy [12.421658400381082]
  We propose a novel and model poisoning attack framework tailored for LDPFL settings.<n>Our approach is driven by the objective of maximizing the global training loss while adhering to local privacy constraints.<n>We evaluate our framework across three representative LDPFL protocols, three benchmark datasets, and two types of deep neural networks.
  arXiv  Detail & Related papers  (2025-09-05T17:23:03Z)
• Securing Genomic Data Against Inference Attacks in Federated Learning Environments [0.31570310818616687]
  Federated Learning (FL) offers a promising framework for collaboratively training machine learning models across decentralized genomic datasets without direct data sharing.<n>While this approach preserves data locality, it remains susceptible to sophisticated inference attacks that can compromise individual privacy.<n>In this study, we simulate a federated learning setup using synthetic genomic data and assess its vulnerability to three key attack vectors.
  arXiv  Detail & Related papers  (2025-05-12T02:36:50Z)
• Towards Robust Federated Learning via Logits Calibration on Non-IID Data [49.286558007937856]
  Federated learning (FL) is a privacy-preserving distributed management framework based on collaborative model training of distributed devices in edge networks. Recent studies have shown that FL is vulnerable to adversarial examples, leading to a significant drop in its performance. In this work, we adopt the adversarial training (AT) framework to improve the robustness of FL models against adversarial example (AE) attacks.
  arXiv  Detail & Related papers  (2024-03-05T09:18:29Z)
• Gaussian Membership Inference Privacy [22.745970468274173]
  We propose a novel and practical privacy notion called $f$-Membership Inference Privacy ($f$-MIP) We derive a family of $f$-MIP guarantees that we refer to as $mu$-Gaussian Membership Inference Privacy ($mu$-GMIP) by theoretically analyzing likelihood ratio-based membership inference attacks on gradient descent (SGD)
  arXiv  Detail & Related papers  (2023-06-12T17:57:05Z)
• Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations [55.2480439325792]
  Federated Learning (FL) facilitates decentralized machine learning model training, preserving data privacy, lowering communication costs, and boosting model performance through diversified data sources. FL faces vulnerabilities such as poisoning attacks, undermining model integrity with both untargeted performance degradation and targeted backdoor attacks. We define a new notion of strong adaptive adversaries, capable of adapting to multiple objectives simultaneously. MESAS is the first defense robust against strong adaptive adversaries, effective in real-world data scenarios, with an average overhead of just 24.37 seconds.
  arXiv  Detail & Related papers  (2023-06-06T11:44:42Z)
• FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning [66.56240101249803]
  We study how hardening benign clients can affect the global model (and the malicious clients) We propose a trigger reverse engineering based defense and show that our method can achieve improvement with guarantee robustness. Our results on eight competing SOTA defense methods show the empirical superiority of our method on both single-shot and continuous FL backdoor attacks.
  arXiv  Detail & Related papers  (2022-10-23T22:24:03Z)
• FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems [15.39058389031301]
  We propose two privacy evaluation metrics designed for FL-based NIDSs. We propose FedDef, a novel optimization-based input perturbation defense strategy with theoretical guarantee. We experimentally evaluate four existing defenses on four datasets and show that our defense outperforms all the baselines in terms of privacy protection.
  arXiv  Detail & Related papers  (2022-10-08T15:23:30Z)
• No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy" [75.98836424725437]
  New methods designed to preserve data privacy require careful scrutiny. Failure to preserve privacy is hard to detect, and yet can lead to catastrophic results when a system implementing a privacy-preserving'' method is attacked.
  arXiv  Detail & Related papers  (2022-09-29T17:50:23Z)
• RelaxLoss: Defending Membership Inference Attacks without Losing Utility [68.48117818874155]
  We propose a novel training framework based on a relaxed loss with a more achievable learning target. RelaxLoss is applicable to any classification model with added benefits of easy implementation and negligible overhead. Our approach consistently outperforms state-of-the-art defense mechanisms in terms of resilience against MIAs.
  arXiv  Detail & Related papers  (2022-07-12T19:34:47Z)
• Optimal Membership Inference Bounds for Adaptive Composition of Sampled Gaussian Mechanisms [93.44378960676897]
  Given a trained model and a data sample, membership-inference (MI) attacks predict whether the sample was in the model's training set. A common countermeasure against MI attacks is to utilize differential privacy (DP) during model training to mask the presence of individual examples. In this paper, we derive bounds for the textitadvantage of an adversary mounting a MI attack, and demonstrate tightness for the widely-used Gaussian mechanism.
  arXiv  Detail & Related papers  (2022-04-12T22:36:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.