The modern technological advancement influences the growth of the cyber–physical system and cyber–social system to a more advanced computing system cyber–physical–social system (CPSS). Therefore, CPSS leads the data science revolution by promoting tri-space information resource from a single space. The establishment of CPSSs increases the related privacy concerns. To provide privacy on CPSSs data, various privacy-preserving schemes have been introduced in the recent past. However, technological advancement in CPSSs requires the modifications of previous techniques to suit its dynamics. Meanwhile, differential privacy has emerged as an effective method to safeguard CPSSs data privacy. To completely comprehend the state-of-the-art developments and learn the field’s research directions, this article provides a comprehensive review of differentially private data fusion and deep learning in CPSSs. Additionally, we present a novel differentially private data fusion and deep learning Framework for Cyber–Physical–Social Systems , and various future research directions for CPSSs.

现代技术进步影响了网络-物理系统和网络-社会系统向更先进的计算系统网络-物理-社会系统 (CPSS) 的发展。因此，CPSS通过从单一空间推广三空间信息资源，引领数据科学革命。CPSS 的建立增加了相关的隐私问题。为了保护 CPSS 数据的隐私，最近引入了各种隐私保护方案。然而，CPSS 的技术进步需要对以前的技术进行修改以适应其动态。同时，差分隐私已成为保护 CPSS 数据隐私的有效方法。全面了解最新进展，了解该领域的研究方向，本文全面回顾了 CPSS 中的差异私有数据融合和深度学习。此外，我们提出了一种用于网络-物理-社会系统的新型差异私有数据融合和深度学习框架，以及 CPSS 的各种未来研究方向。