Enhancing Group Privacy for Effective CIoT Data Mining in 6G Networks Using Differential Privacy

Bo Ning, Xinjian Zhang, Chengfei Liu, Ning Wang, Guanyu Li

Abstract: In the realm of 6G wireless networks, the Consumer Internet of Things (CIoT) aims to revolutionize consumer electronics by integrating advanced technologies such as Artificial Intelligence (AI). As CIoT environments become more dynamic and data-rich, ensuring data privacy while maintaining the utility of data mining is crucial. Graph data mining, which models object relationships with expressive capabilities, is central to this transformation but may pose data leakage risks. Traditional differential privacy methods in graphs often focus on releasing attribute or node information or adding excessive noise, reducing data availability. Therefore our focus is on protecting group relationships and leveraging their closeness to design budget allocation mechanisms that reduce noise. The proposed model uses k-cores to hierarchically manage group relationships, considering the variation in edges between k-cores and (k-1)-cores. Significant changes indicate a high degree of aggregation and interdependence at higher levels, warranting greater privacy protection. By allocating the privacy budget according to edge levels, our approach balances protection and data availability. Experiments on four types of graphs show that the network generated by our model accurately represents the original graph, making it suitable for subsequent data mining applications.

External IDs:dblp:journals/tce/NingZLWL25