# Private Dataset for AI Models AI models, especially LLMs, need access to more personalized data to improve the performance, as public datasets are exhausted nowadays. However, the traditional way of using private data like user behavior and transaction history, suffers from privacy and integrity challenges. zkTLS-based data proofs offers a secure and verifiable way to access high-quality private data for AI model training. In particular, data proofs address the issues by allowing AI developers to verify the provenance and authenticity of private data without revealing privacy-compromising details. It enhances the value of data shared for training by making it verifiable, which significantly increases its economic worth. Verifiable data also mitigates the risks of data poisoning or manipulation during the training process. zkTLS-based data proofs have compelling economic implications. They enable users to retain control over their private data, selectively sharing only what they choose in a competitive marketplace where AI developers strive to build the best models. This approach unlocks new opportunities for AI applications in privacy-sensitive fields such as healthcare, parenting, and finance. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://nexaid.gitbook.io/nexaid/implementation-scenarios/private-dataset-for-ai-models.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.