Ensuring reliability in adversarial settings necessitates treating privacy as a foundational component of data-driven systems. While differential privacy and cryptographic protocols offer strong guarantees, existing schemes rely on a fixed privacy budget, leading to a rigid utility-privacy trade-off that fails under heterogeneous user trust. Moreover, noise-only differential privacy preserves geometric structure, which inference attacks exploit, causing privacy leakage. We propose TADP-RME (Trust-Adaptive Differential Privacy with Reverse Manifold Embedding), a framework that enhances reliability under varying levels of user trust. It introduces an inverse trust score in the range [0,1] to adaptively modulate the privacy budget, enabling smooth transitions between utility and privacy. Additionally, Reverse Manifold Embedding applies a nonlinear transformation to disrupt local geometric relationships while preserving formal differential privacy guarantees through post-processing. Theoretical and empirical results demonstrate improved privacy-utility trade-offs, reducing attack success rates by up to 3.1 percent without significant utility degradation. The framework consistently outperforms existing methods against inference attacks, providing a unified approach for reliable learning in adversarial environments.

翻译：在对抗性环境下确保可靠性需将隐私视为数据驱动系统的基石。尽管差分隐私与密码学协议能提供强安全性保障，但现有方案依赖于固定隐私预算，导致刚性且无法适应异构用户信任的效用-隐私权衡。此外，纯噪声型差分隐私保留的几何结构会遭受推理攻击利用，引发隐私泄露问题。我们提出TADP-RME（信任自适应差分隐私与反向流形嵌入）框架，用于在用户信任度动态变化场景下增强系统可靠性。该框架引入取值范围为[0,1]的逆信任分数来自适应调节隐私预算，实现效用与隐私间的平滑过渡。同时，反向流形嵌入通过非线性变换破坏局部几何关系，并通过后处理保留形式化差分隐私保证。理论与实验结果表明，该方法改善了隐私-效用权衡，在未显著降低效用的前提下，将攻击成功率最高降低3.1%。该框架在抵御推理攻击方面持续优于现有方法，为对抗性环境中的可靠学习提供了统一方案。