Constrained-Based Differential Privacy: Releasing Optimal Power Flow Benchmarks Privately - Releasing Optimal Power Flow Benchmarks Privately

This paper considers the problem of releasing optimal power flow benchmarks that maintain the privacy of customers (loads) using the notion of Differential Privacy. It is motivated by the observation that traditional differential-privacy mechanisms are not accurate enough: The added noise fundamentally changes the nature of the underlying optimization and often leads to test cases with no solution. To remedy this limitation, the paper introduces the framework of Constraint-Based Differential Privacy (CBDP) that leverages the post- processing immunity of differential privacy to improve the accuracy of traditional mechanisms. More precisely, CBDP solves an optimization problem to satisfies the problem-specific constraints by redistributing the noise. The paper shows that CBDP enjoys desirable theoretical properties and produces orders of magnitude improvements on the largest set of test cases available.

本文考虑了使用差异隐私概念来释放最佳功率流基准（负载）的最佳功率流基准的问题。观察到传统的差异性机制不够准确：增加的噪声从根本上改变了基础优化的性质，并且通常导致没有解决方案的测试用例。为了解决这一限制，本文介绍了基于约束差异隐私（CBDP）的框架，该框架利用了差异隐私的后处理免疫，以提高传统机制的准确性。更确切地说，CBDP解决了一个优化问题，以通过重新分布噪声来满足特定问题的约束。该论文表明，CBDP具有理想的理论特性，并在可用的最大测试用例上产生了数量级的改善。