Often we consider machine learning models or statistical analysis methods which we endeavour to alter, by introducing a randomized mechanism, to make the model conform to a differential privacy constraint. However, certain models can often be implicitly differentially private or require significantly fewer alterations. In this work, we discuss Determinantal Point Processes (DPPs) which are dispersion models that balance recommendations based on both the popularity and the diversity of the content. We introduce DPPs, derive and discuss the alternations required for them to satisfy epsilon-Differential Privacy and provide an analysis of their sensitivity. We conclude by proposing simple alternatives to DPPs which would make them more efficient with respect to their privacy-utility trade-off.

翻译：通常，我们考虑对机器学习模型或统计分析方法进行改造，通过引入随机化机制使其满足差分隐私约束。然而，某些模型本身可能已具备隐含的差分隐私特性，或仅需极少修改即可满足要求。本文探讨行列式点过程（DPPs）——一种基于内容流行度与多样性进行平衡推荐的分散模型。我们介绍了DPPs的基本原理，推导并讨论了使其满足ε-差分隐私所需的改进方案，并对其敏感度进行了分析。最后，我们提出了DPPs的若干简化替代方案，以优化其隐私保护效用与模型性能的平衡关系。