论文标题
ARA：集中式差异隐私的汇总和分析
ARA : Aggregated RAPPOR and Analysis for Centralized Differential Privacy
论文作者
论文摘要
差异隐私（DP）现在已成为敏感统计数据分析的标准。 DP中的两个主要方法是本地和中央。两种方法在数据存储，要分析的数据量，分析，速度等方面都有明显的差距。当地的速度获胜。我们已经测试了最先进的标准差异状态，这是一种本地方法，并支持了这一差距。我们的工作也完全关注这一部分。在这里，我们提出了一个模型，该模型最初收集了从多个客户端收集的拉波尔报告，然后将其推向TF-IDF估计模型。然后，TF-IDF估计模型根据特定位置的“位”及其对该位置的贡献估算报告。因此，它从多个客户产生了集中的差异隐私分析。我们的模型每次都成功有效地分析了主要的真实价值。
Differential privacy(DP) has now become a standard in case of sensitive statistical data analysis. The two main approaches in DP is local and central. Both the approaches have a clear gap in terms of data storing,amount of data to be analyzed, analysis, speed etc. Local wins on the speed. We have tested the state of the art standard RAPPOR which is a local approach and supported this gap. Our work completely focuses on that part too. Here, we propose a model which initially collects RAPPOR reports from multiple clients which are then pushed to a Tf-Idf estimation model. The Tf-Idf estimation model then estimates the reports on the basis of the occurrence of "on bit" in a particular position and its contribution to that position. Thus it generates a centralized differential privacy analysis from multiple clients. Our model successfully and efficiently analyzed the major truth value every time.
