TL;DR
The analysis finds that noise-based methods such as DP remain the practical baseline but offer only partial protection; cryptographic approaches provide stronger theoretical guarantees at substantially higher cost; and LLM/multimodal leakage remains an urgent, under-benchmarked gap.
Machine learning systems use sensitive personal data to train and infer models, but developers are largely unaware of privacy attack vectors that evade conventional data security controls. This paper surveys the privacy threat landscape inherent to ML workflows, including membership inference attacks, model inversion a...
A federated deep learning framework that systematically integrates adaptive privacy noise mechanisms and trust-weighted aggregation within a distributed architecture that ensures the protection of sensitive data during collaborative analysis through precise differential privacy control and advanced neural network model...
Knowledge distillation (KD) is increasingly used in federated learning (FL) because it enables clients to exchange predictions, features, prototypes, or synthetic knowledge rather than full model parameters. This change can reduce communication and support heterogeneous models, but it also changes the privacy and secur...
SecureFedShield is proposed, a privacy-preserving federated learning framework designed for secure financial fraud detection in adversarial environments that integrates adaptive privacy protection, trust-aware client evaluation, adversarial update detection, and robust model aggregation into a unified architecture.
This paper presents a systematic framework for membership inference attacks, in which an adversary with only black-box query access to a deployed classifier determines whether a specific individual's record was part of its training set.
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