Evolving privacy and utility in data storage and publishing

该项目旨在开发一个基于分布式进化计算的框架，以优化分布式数据库系统中的数据隐私和效用。它旨在同步解决多目标、动态和多任务场景中隐私保护和效用维护的冲突挑战。预期成果包括一个新的计算框架即服务，以及免费提供的分布式计算模型、进化算法和知识转移战略。预期的好处包括对人工智能、网络安全、分布式计算的理论贡献，以及消除数据所有者隐私担忧、同时保证数据在进一步利用中的价值的服务。

This project aims to develop a distributed evolutionary computation-based framework to optimize data privacy and utility in distributed database systems. It intends to synchronously solve the conflicting challenges of privacy preservation and utility maintenance in multi-objective, dynamic, and multitasking scenarios. Expected outcomes include a new computation framework as a service and freely available distributed computation models, evolutionary algorithms, and knowledge-transfer strategies. Anticipated benefits include theoretical contributions to artificial intelligence, cyber security, distributed computation, and a service to eliminate data owners’ privacy concerns while guaranteeing the value of data in further utilization.