Current differential privacy frameworks face significant challenges: vulnerability to correlated data attacks and suboptimal utility-privacy tradeoffs. To address these limitations, we establish a novel information-theoretic foundation for Dalenius' privacy vision using Shannon's perfect secrecy framework. By leveraging the fundamental distinction between cryptographic systems (small secret keys) and privacy mechanisms (massive datasets), we replace differential privacy's restrictive independence assumption with practical partial knowledge constraints ($H(X) \geq b$). We propose an information privacy framework achieving Dalenius security with quantifiable utility-privacy tradeoffs. Crucially, we prove that foundational mechanisms -- random response, exponential, and Gaussian channels -- satisfy Dalenius' requirements while preserving group privacy and composition properties. Our channel capacity analysis reduces infinite-dimensional evaluations to finite convex optimizations, enabling direct application of information-theoretic tools. Empirical evaluation demonstrates that individual channel capacity (maximal information leakage of each individual) decreases with increasing entropy constraint $b$, and our framework achieves superior utility-privacy tradeoffs compared to classical differential privacy mechanisms under equivalent privacy guarantees. The framework is extended to computationally bounded adversaries via Yao's theory, unifying cryptographic and statistical privacy paradigms. Collectively, these contributions provide a theoretically grounded path toward practical, composable privacy -- subject to future resolution of the tradeoff characterization -- with enhanced resilience to correlation attacks.

翻译：当前差分隐私框架面临两大挑战：对关联数据攻击的脆弱性以及次优的效用-隐私权衡。为突破这些局限，我们基于香农完美保密理论为Dalenius隐私愿景建立了全新的信息论基础。通过利用密码系统（小规模密钥）与隐私机制（海量数据集）的本质差异，我们以实用的部分知识约束（$H(X) \geq b$）替代差分隐私中严格的独立性假设。本文提出的信息隐私框架在实现Dalenius安全的同时具备可量化的效用-隐私权衡特性。关键性贡献在于：我们证明了随机响应机制、指数机制与高斯机制等基础隐私通道在满足Dalenius要求的同时，仍能保持群组隐私性与组合性质。通过通道容量分析，我们将无限维评估转化为有限维凸优化问题，使信息论工具得以直接应用。实证研究表明：个体通道容量（每个个体的最大信息泄露量）随熵约束$b$增大而递减，且在同等隐私保障下，本框架相较于经典差分隐私机制实现了更优的效用-隐私权衡。借助姚氏理论，该框架可扩展至计算受限的敌手场景，从而统一密码学与统计学隐私范式。总体而言，这些贡献为构建实用化、可组合的隐私保护体系奠定了理论基础——在权衡特性表征问题获得最终解决的前提下——显著增强了对关联攻击的防御能力。