Privacy Protection Scheme for Cyberspace Mapping Data Based on Differential Privacy
Yue Zhang, Guanlin Si, Bin Dong, Leran Chen, Xiaotian Xu · 2024
Privacy protection in cyberspace mapping data is of paramount importance in today's digital landscape. This paper introduces a method for safeguarding privacy in cyberspace mapping data using the principles of differential privacy. In response to growing concerns regarding data privacy and security, our research addresses the critical need for robust privacy protection mechanisms during cyberspace mapping activities. Leveraging the principles of differential privacy, our method ensures individual privacy preservation while enabling meaningful analysis of cyberspace mapping data. We explore the optimization of privacy budget allocation and the applicability of differential privacy techniques in diverse cyberspace mapping scenarios. Our findings underscore the importance of privacy-preserving practices in cyberspace mapping and highlight the potential of leveraging differential privacy to enhance the security and integrity of digital networks.