Privacy-Preserving Deep Learning Models For Alcoholism Diagnosis Through EEG Data Analysis Using Differential Privacy ‎Mechanisms - M, Varalatchoumy; Reddy , A. Srinivasa; Imambi, S .Sagar; Ameta, Gaurav Kumar; Thaiyalnayaki, K .; Logesh , S. K.; Dhivya, S .; Mu‎thulakshmi, K (International Journal of Basic and Applied Sciences, 2025)
Alcoholism diagnosis through electroencephalogram (EEG) data analysis offers a promising alternative to traditional methods by identifying ‎specific brain activity patterns associated with alcohol dependency. While deep learning techniques have demonstrated high accuracy in ‎classifying EEG signals, privacy concerns related to sensitive medical data remain prevalent. Ensuring the privacy of patient data is critical ‎for building trust and enabling the adoption of these tools in real-world clinical settings. This study develops deep learning models with ‎enhanced privacy guarantees by incorporating differential privacy mechanisms, including (ϵ, δ)-Differential Privacy and Gaussian Differential Privacy (GDP). We compare their efficacy in preserving data privacy while maintaining model utility. Experiments show that convolu-‎tional and long-short-term-memory models optimized with Adam excel in utility and stability. GDP outperforms (ϵ, δ)-DP by requiring less ‎noise, while DP-Adam surpasses DP-SGD in privacy and utility, particularly for fast convergence. Larger datasets further enhance this ‎balance, emphasizing the importance of effective privacy mechanisms and sufficient data. By balancing privacy and utility, this work con-‎contributes a novel approach to privacy-preserving AI for sensitive health applications, emphasizing scalable models that maintain diagnostic ‎accuracy‎.