论文标题
私人外包贝叶斯优化
Private Outsourced Bayesian Optimization
论文作者
论文摘要
本文介绍了私人淘汰的高斯流程 - 加工信心（PO-GP-UCB）算法，该算法是在外包环境中具有可证明性能保证的外包设置中的第一种保存隐私贝叶斯优化（BO）的算法。我们考虑持有数据集和执行实体BO的实体由不同各方表示，并且数据集不能非释放数据集。例如，医院持有敏感医疗记录的数据集，并将该数据集上的BO任务外包给工业AI公司。我们方法的关键思想是使我们的算法的BO性能类似于使用原始数据集的非私有GP-UCB运行的算法，这是通过使用基于随机投射的转换来实现的，该转换可以保持隐私和输入之间的成对距离。我们的主要理论贡献是表明，可以为我们的PO-GP-UCB算法建立类似于标准GP-UCB算法的遗憾。我们从经验上评估了使用合成和现实世界数据集的PO-GP-UCB算法的性能。
This paper presents the private-outsourced-Gaussian process-upper confidence bound (PO-GP-UCB) algorithm, which is the first algorithm for privacy-preserving Bayesian optimization (BO) in the outsourced setting with a provable performance guarantee. We consider the outsourced setting where the entity holding the dataset and the entity performing BO are represented by different parties, and the dataset cannot be released non-privately. For example, a hospital holds a dataset of sensitive medical records and outsources the BO task on this dataset to an industrial AI company. The key idea of our approach is to make the BO performance of our algorithm similar to that of non-private GP-UCB run using the original dataset, which is achieved by using a random projection-based transformation that preserves both privacy and the pairwise distances between inputs. Our main theoretical contribution is to show that a regret bound similar to that of the standard GP-UCB algorithm can be established for our PO-GP-UCB algorithm. We empirically evaluate the performance of our PO-GP-UCB algorithm with synthetic and real-world datasets.
