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.