SBIR Phase I: Accelerating Machine Learning on Encrypted Data

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The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to enable organizations to collaborate and extract insights from data without revealing private or proprietary information. Data-driven innovations benefit all aspects of our lives, ranging from better healthcare and increased productivity to smarter energy consumption. Artificial intelligence (AI) has so much potential but access to data has become the major bottleneck. Improved data security and privacy techniques enabled by this proposal will remove barriers to realizing these benefits. Commercialization of the proposed technology will have major implications for the cloud computing and data analytics market. The proposed technology eliminates the security risks that prevent companies from moving computations to the cloud, which offers affordable and scalable data processing infrastructure. Furthermore, organizations can collaborate on sensitive data without revealing the underlying information.This Small Business Innovation Research Phase I project will enable machine learning and AI on encrypted data based on Fully-Homomorphic Encryption (FHE). Current approaches are too slow and difficult to use; therefore, they have limited applicability in enterprise settings. This Phase I project extends current approaches by designing a hardware-accelerated FHE service that meets mandated security standards and targets enterprise AI applications. To advance translation quickly, off-the-shelf accelerators will be used, which are available in major cloud computing data centers. Further performance gains will be achieved through neural network optimizations that reduce the computation overhead of encrypted computations. In addition to the performance improvements, software integration with common machine learning frameworks will be implemented to lower the barrier for usability.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.