In this paper, we present a framework for privacy preserving collaborative data analysis among multiple data providers acting as edge of a cloud environment. The proposed framework computes the best trade-off among privacy and result accuracy, based on the privacy requirements of data providers and the specific requested analysis algorithm. Though the presented model is general and can be applied to different environments, this work is motivated by the need of sharing information related to Cyber Threats (CTI). The presented framework is independent from the number of data providers, used data format, privacy requirement and analysis operations. The model is based on the concepts of trade-off score between accuracy and privacy, which also considers measures for privacy requirement such as differential privacy, l-diversity and k-anonymity. Together with the model, the paper discusses the framework implementation and presents results to show the effectiveness and viability of the proposed approach.

在本文中，我们提出了一个框架，该框架用于在充当云环境边缘的多个数据提供者之间保留隐私保护协作数据分析。所提出的框架根据数据提供者的隐私要求和特定的请求分析算法来计算隐私和结果准确性之间的最佳平衡。尽管提出的模型是通用的并且可以应用于不同的环境，但是这项工作是出于共享与网络威胁（CTI）有关的信息的需要而进行的。提出的框架与数据提供者的数量，使用的数据格式，隐私要求和分析操作无关。该模型是基于精度和隐私之间折衷得分，这也认为隐私要求的措施作为差分隐私，如概念升-多样性和k-匿名性。连同模型一起，本文讨论了框架的实现并提供了结果，以证明所提出方法的有效性和可行性。