Vaultree is deeply committed to advancing the field of data encryption while maintaining a responsible and transparent approach to security. Our innovations, particularly in searchable encryption, FHE and encrypted ML exemplify our dedication to creating practical and secure solutions.

For those interested in the technical specifics, Vaultree has developed a comprehensive approach to searchable encryption, detailed in our patent (EP4000213A1). This method enables efficient and secure search operations on encrypted data, ensuring that sensitive information remains protected without sacrificing usability. You can explore the full details of our patent here: https://patents.google.com/patent/EP4000213A1/en?q=(Vaultree...

Additionally, our work on Fully Homomorphic Encryption (FHE) represents a significant leap forward in the field. We've published our FHE scheme in the IACR ePrint archive, where it is accessible for review and further academic scrutiny. You can find the publications here: https://eprint.iacr.org/2024/1105 and https://eprint.iacr.org/2024/1622

Moreover, we are actively working on encrypted Machine Learning (ML) implementations. To contribute to the broader community, we've open-sourced our VENumML library, which is based on Vaultree's FHE. This library aims to enable secure and private ML operations on encrypted data, pushing the boundaries of what is possible in privacy-preserving technologies.

Vaultree's commitment to innovation and responsible encryption practices ensures that we not only keep data secure but also advance the field with solutions that are both effective and practical for real-world applications.