Maryland Patent of the Month – March 2024
The field of secure data processing has witnessed significant advancements with the introduction of encryption techniques like homomorphic encryption. Homomorphic encryption uses a specific algebraic operation performed on plaintext. Enveil, Inc, a pioneering innovator in Privacy Enhancing Technology, has secured a patent for their novel approach to homomorphic encryption for secure data processing.
In this innovative method, Enveil enables secure transmission of machine learning data structures from a client to remote servers for analysis. These data structures, encrypted using a homomorphic encryption scheme, encapsulate valuable insights derived from trained machine learning models, such as neural networks or decision trees. The encryption scheme, including fully homomorphic encryption variants like Brakerski/Fan-Vercauteren and Cheon-Kim-Kim-Song cryptosystems, ensures that sensitive information remains protected throughout the analysis process.
Upon receiving the encrypted data structures, the servers extract previously unseen instances of data for evaluation. Using the trained machine learning models contained within the encrypted structures, the servers perform analyses to generate encrypted results pertaining to the provided data instances. These encrypted results are then securely transmitted back to the client, where they can be decrypted using the homomorphic encryption scheme.
This method offers a robust solution for organizations handling sensitive data, such as financial records or medical information, by providing end-to-end security for machine learning analytics. By safeguarding data both in transit and during processing, Enveil’s innovation addresses critical concerns surrounding data privacy and confidentiality.
The versatility of this method allows for the analysis of various types of machine learning models, including regression models and neural networks, thereby catering to diverse analytical needs across different domains. Additionally, the utilization of binary feature vectors enhances computational efficiency while maintaining the confidentiality of sensitive data.
Enveil’s commitment to advancing secure analytics through homomorphic encryption underscores its dedication to providing cutting-edge solutions in the realm of data privacy and security. With the continuous evolution of technology, innovations like these play a pivotal role in shaping the future landscape of secure data processing and machine learning analytics.
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