2-2-1. Data Privacy Protection

From AI video and image processing to highly confidential projects, the biggest challenge of distributed computing is privacy protection. There is a problem with current cloud computing systems that they cannot fully ensure the confidentiality of data during computation.

EMETH has developed distributed confidential computing technology to address this issue. This technology performs advanced computational processes while ensuring the confidentiality of data. It splits encrypted learning data, distributes it to multiple nodes, and each node processes the data while it remains encrypted. After processing, the generated model is securely integrated, ensuring the privacy and confidentiality of the data.

EMETH's distributed confidential computing significantly reduces security risks and balances privacy protection with high performance. Parallel computing through distributed processing effectively handles large-scale data, improving productivity in AI development and applications.

This groundbreaking technology promotes the development of innovative AI services that safely utilize confidential data and contributes to the protection of personal information and the establishment of data sovereignty. EMETH opens new possibilities in distributed computing and realizes a future of data utilization that is more secure and reliable.