The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to enhance data security by solving the current problem of securing data in use through encryption. It has the potential to build trust on the cloud by overcoming the technical challenges of building a fully homomorphic encryption solution. This will allow data owners to encrypt their confidential data and process that encrypted data without constraints or having to decrypt it first. This important new capability will enable further data migration to the cloud and a new secure Encrypted Data-as-a-Service (EDaaS) model where: (i) confidential data can be used for analyses or machine learning while encrypted and as a service, without ever being decrypted or shared without encryption; and (ii) data owners can authorize users to access their encrypted data while maintaining control and authority. In short, data owners will be able to securely migrate confidential data to the cloud and maintain full privacy and security, even for commercial use. This Small Business Innovation Research (SBIR) Phase I project will investigate and validate the feasibility and performance of an innovative obfuscation method that can be applied to allow the unconstrained processing of encrypted data. This patent-pending method is based on reversible computing to build homomorphic encryption functions by obfuscating bare functions combined with encryption/decryption operators. Libraries of these functions can then serve as a building block for generic and specialized calculations on encrypted data. To prove feasibility, this Phase I project addresses three technical hurdles: (i) validation of the method to ensure the security of data and functions, (ii) optimization of performance relative to existing homomorphic encryption schemes, and (iii) connectivity to existing analytical tools and cloud environments to host and process data. The goal of this research project is to develop a method for simple, fast, and flexible homomorphic encryption that can be integrated with existing analytical processes in private as well as public environments, such as the cloud, for commercial success. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
