Brand Design Data Security and Privacy Protection Under 6G Network Slicing Architecture

Abstract

The rapid growth of networking technology has generated several situations and issues in the field of safeguarding critical brand design data in the present hyper connected context, particularly with the arrival of the 6^th Generation (6G). As brand development relies more on cloud-based services, protecting client data and intellectual property (IP) is essential. By using 6G network slicing architecture, which contains dedicated, secure network sections for brand design services, improved encryption, and anomaly detection systems, the research suggested a solution to such issues. The data includes features such as network performance, security measurements, and user data privacy measures. The methodology entails pre-processing brand design data with Z-score normalization to standardize feature distributions, followed by Principal Component Analysis (PCA) for a decrease of dimensions. The proposed method uses a Fully Homomorphic Encryption Driven Quantum Support Vector Machine (FHE-QSVM) to detect anomalies in real time while assuring safe and efficient resource allocation in dedicated slices. FHE-QSVM anomaly detection model produced significant metrics, with accuracy (98%), recall (96%), precision (97%), and F1-score (96%) data by accurately categorizing threats while maintaining data confidentiality. The finding shows the FHE-QSVM enhances both the security and privacy of brand design data by accurately categorizing threats while maintaining data confidentiality. Overall, this strategy offers a scalable solution for secure AI-powered brand design services, highlighting the importance of creative encryption, real-time monitoring, and 6G network slicing to meet contemporary data security standards.