Personalizing ChatGPT Responses Using Differential Privacy Techniques

In today’s digital world, advancements in artificial intelligence (AI) and natural language processing (NLP) have significantly improved user interactions with computer systems. Models like ChatGPT, capable of generating high-quality text and accurate responses, have enhanced user experiences. However, personalizing responses to provide a more tailored experience poses challenges such as privacy violations. Differential privacy techniques, by adding controlled noise to data, offer an effective solution to mitigate these risks. This study explores the process of personalizing ChatGPT’s responses using differential privacy techniques. The performance of SVM, Random Forest, and XGBoost algorithms was evaluated on the Dialogs, AmbigQA, and Break datasets. The XGBoost algorithm showed the best performance on the Break dataset, achieving an accuracy of 97.8% and RMSE of 0.12. SVM achieved 95.6% accuracy and MAE of 0.18 on the AmbigQA dataset, while Random Forest reached 93.4% accuracy and RMSE of 0.15 on the Dialogs dataset. Laplace and Gaussian noise were added to the data to ensure privacy, resulting in a precision reduction of less than 2%. The type of noise affected algorithms differently: Gaussian noise had a lesser impact on XGBoost, while Laplace noise was more optimal for SVM and Random Forest. This study demonstrates that it is possible to personalize responses while maintaining user privacy.