Privacy preserving and data utility in outsourced systems

在数据隐私和实用性之间做出最佳权衡是隐私保护外包环境中的一个重要挑战。 该项目旨在开发一个平衡的分布式框架，以实现外包数据的最佳效用，同时保护私人信息。 该框架包括分布式进化算法的总体结构、高效的预定义拓扑结构和动态分组重组模型。该项目的成果将有利于在该国的应用，因为它结合了新兴技术提出的新的隐私限制和实用程序要求，以更好地保护敏感信息和最大限度地利用外包系统中的数据。

Making the best tradeoff between data privacy and utility is a vital challenge in privacy-preserving outsourcing environments. This project aims to develop a balanced distributed framework to achieve the best utility of outsourced data while protecting private information. The framework consists of general structure of distributed evolutionary algorithms and a predefined topology for high optimization efficiency and a dynamic grouping recombination model. The project outcomes will be beneficial to applications in the nation as it incorporates new privacy constraints and utility requirements raised by emerging technologies to enable better protection of sensitive information and maximal data utility in outsourced systems.