## 4 Applications of AI in Modern Communication Networks
### Security and Privacy
#### 4.3.2 Encryption and Privacy-Preserving Techniques  
Artificial intelligence (AI) plays a significant role in enhancing encryption methods and privacy-preserving techniques, addressing the growing concerns of security and privacy in communication networks [104]. As the volume and complexity of data traffic continue to increase, traditional encryption algorithms face challenges in adapting to dynamic network conditions and ensuring both strong security and optimal performance [105]. AI provides solutions by making encryption mechanisms more adaptive, intelligent, and responsive to real-time conditions. * AI-Driven Adaptive Encryption: One of the primary ways AI is used to enhance encryption is through adaptive encryption schemes [103]. In traditional encryption methods, the encryption keys are typically fixed or based on pre-determined rules. However, in dynamic communication networks, network conditions such as bandwidth, latency, and congestion can vary significantly. AI-based systems can dynamically adjust encryption keys and parameters based on these conditions, optimizing the trade-off between encryption strength and system performance [106]. For example, machine learning algorithms, particularly reinforcement learning models, can continuously monitor network performance and adjust encryption protocols to balance security and computational overhead [99]. These models can learn optimal encryption strategies for different types of data traffic, ensuring robust security without introducing the security and privacy-preserving techniques. Figure 6: Comparison of AI-based Transformer-based intrusion detection system and traditional IDS in terms of detection accuracy. ing significant latency or bandwidth consumption. By using AI to analyze real-time network traffic patterns, encryption can be more intelligent, automatically adjusting to the nature of the communication being transmitted, whether it is video, voice, or data [107]. * AI for Privacy-Preserving Techniques: In addition to enhancing encryption, AI is instrumental in developing advanced privacy-preserving techniques. Privacy concerns in communication networks are at an all-time high, with personal data being exchanged more frequently than ever [108]. Privacy-preserving protocols, such as differential privacy, have been enhanced with AI to anonymize sensitive information while allowing for meaningful data analysis [109]. Machine learning techniques such as federated learning are gaining traction as privacy-preserving methods in distributed systems [110]. In federated learning, models are trained across decentralized devices using local data, and only the model updates are shared across the network, not the raw data themselves [111]. This prevents sensitive data from leaving the local device, ensuring user privacy while still enabling the machine learning models to improve over time [112]. This technique is particularly useful in scenarios like mobile networks and Internet of Things (IoT) systems, where privacy is critical, and centralized data collection is impractical [113; 114]. Moreover, AI can also be used to detect and mitigate potential privacy leaks in communication protocols [115]. Using anomaly detection and pattern recognition, AI models can identify unusual behavior in data transmissions that may indicate the exposure of sensitive information, enabling more proactive measures to prevent data breaches or unauthorized access. * AI in Secure Multi-Party Computation: AI is also making strides in securing collaborative computations where multiple parties need to share their data for collective processing while maintaining the confidentiality of their inputs [116]. Secure Multi-Party Computation (SMPC) protocols are often computationally expensive and difficult to scale. However, AI can optimize the process of encrypting and processing data in parallel, reducing the computational load while maintaining high levels of privacy and security [117].