Privacy preserving and data utility in outsourced systems

在保护隐私的外包环境中，在数据隐私和实用性之间做出最佳权衡是一项至关重要的挑战。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。该框架由分布式进化算法的总体结构、优化效率高的预定义拓扑结构和动态分组重组模型组成。该项目的成果将有利于国家的应用，因为它结合了新兴技术提出的新的隐私约束和效用要求，以便更好地保护敏感信息和外包系统中的最大数据效用。

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.