论文标题
用于能源系统中差异私有优化的二重性优化
Bilevel Optimization for Differentially Private Optimization in Energy Systems
论文作者
论文摘要
本文研究了如何将差异隐私应用于投入敏感的受约束优化问题。由于输入数据的随机扰动通常会使受约束的优化问题不可行或显着改变其最佳解决方案的性质，因此这项任务引起了重大挑战。为了解决这一难度，本文提出了一个可以用作后处理步骤的双重优化模型：它重新分布了由差异私有机制最佳地引入的噪声，同时还可以恢复可行性和近乎典型性。该论文表明，在自然假设下，可以为使用敏感客户数据的现实大规模非线性非凸优化问题有效地解决此二元模型。实验结果证明了隐私机制的准确性，与标准方法相比，展示了显着的好处。
This paper studies how to apply differential privacy to constrained optimization problems whose inputs are sensitive. This task raises significant challenges since random perturbations of the input data often render the constrained optimization problem infeasible or change significantly the nature of its optimal solutions. To address this difficulty, this paper proposes a bilevel optimization model that can be used as a post-processing step: It redistributes the noise introduced by a differentially private mechanism optimally while restoring feasibility and near-optimality. The paper shows that, under a natural assumption, this bilevel model can be solved efficiently for real-life large-scale nonlinear nonconvex optimization problems with sensitive customer data. The experimental results demonstrate the accuracy of the privacy-preserving mechanism and showcases significant benefits compared to standard approaches.
