PrivSynth：面向合成数据隐私与效用的交替式与控制论优化
PrivSynth: Alternating and Control-Based Optimization for Privacy and Utility in Synthetic Data
把合成数据生成建模为隐私-效用的双目标优化，并用离散时间最优控制（PMP）求解数据选择参数，为视觉研究者提供了一套可复用的、在生成模型训练中显式抑制成员推理与重建泄露的优化框架。
原文依据 synthetic data can still leak private information via membership inference and reconstruction attacks
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论随着公开可用数据日益减少，合成数据生成（SDG）已成为隐私保护数据共享的一种实用方案。通过在私有数据上训练生成模型，SDG 生成的样本在保留任务相关特征的同时混淆敏感内容。然而，近期工作表明合成数据仍可通过成员推理与重建攻击泄露隐私信息。现有防御方法往往会损害下游效用。为应对隐私-效用权衡，我们将 SDG 建模为双目标优化问题。然而，难以处理的梯度与昂贵的子集评估构成主要挑战。我们通过对生成模型与数据选择参数进行交替优化来解决该问题，并进一步将选择步骤重构为离散时间最优控制问题，使用庞特里亚金最大值原理求解。我们提出 PrivSynth，一个量化多种隐私风险并将其整合进控制目标的框架。理论分析保证了收敛性，在基准与医学数据集上的实验表明，PrivSynth 相比最优方法取得了更好的效用与更强的隐私保护。
英文原文摘要 1191 字符
As publicly available data dwindles, synthetic data generation (SDG) has become a practical solution for privacy-preserving data sharing. By training generative models on private data, SDG creates samples that retain task-relevant features while obfuscating sensitive content. However, recent work shows that synthetic data can still leak private information via membership inference and reconstruction attacks. Existing defenses often degrade downstream utility. To address the privacy-utility trade-off, we formulate SDG as a bi-objective optimization problem. Yet, intractable gradients and expensive subset evaluation pose major challenges. We address this via alternate optimization over the generative model and data selection parameter, and further recast the selection step as a discrete-time optimal control problem, solved using Pontryagin's Maximum Principle. We propose PrivSynth, a framework that quantifies multiple privacy risks and integrates it into the control objective. Theoretical analysis guarantees convergence, and experiments on benchmark and medical datasets show that PrivSynth achieves better utility and stronger privacy protection than state-of-the-art methods.