Neither ChatGPT nor Google Gemini are inherently designed to perform privacy-preserving Machine Learning (PPML) in their core functions. Instead, they are large language models (LLMs) primarily focused on text generation and understanding, often trained on vast public datasets. While they can assist developers in understanding and implementing PPML techniques by explaining concepts or generating code snippets, they do not intrinsically offer features like differential privacy, federated learning, or homomorphic encryption. A key consideration for both is their reliance on user input; without explicit safeguards or enterprise versions, there's a risk of submitted data being processed or used for further training, which contradicts PPML principles. Therefore, their performance for PPML is not about direct execution but rather about their utility as powerful assistants in the development of privacy-conscious systems, provided users implement robust privacy measures independent of the LLM. More details: https://kharbit-group.com/Home/ChangeCulture?langCode=ar&returnUrl=https://infoguide.com.ua/