SaTC: CORE: Small: Accelerating Privacy Preserving Deep Learning for Real-time Secure Applications

Currently, to draw insights from data, the owner needs to send them to a cloud server to perform complex Machine Learning based analytics. To enable data security, the data is encrypted by the owner and sent to the cloud server where it is decrypted to perform analytics. For privacy sensitive applications such as healthcare, finance, etc., this leads to data security concerns as the decrypted data on the cloud may be snooped by malicious actors.

To address this concern, this proposal will develop techniques to efficiently perform Machine Learning (ML) analytics on encrypted data, without a need for decoding, thereby enabling end-to-end privacy.

The proposed project will develop optimizations targeting Field Programmable Gate Arrays (FPGAs) to address the challenges such as conflicts in parallel access to shared objects, irregular memory accesses, low data reuse, etc., which are prevalent in many application domains. Moreover, the parameterized FPGA Intellectual Property (IP) cores for the key kernels of privacy preserving Deep Neural Networks (DNNs) such as Number Theoretic Transform (NTT), rotation, multiplication, etc., that will be developed in the project will allow application developers to easily implement a wide variety of privacy preserving Machine Learning/Deep Learning models.

Additionally, the proposed acceleration techniques are applicable to applications which rely on post-quantum lattice based cryptography.

The broader impact of this work is in efficient use of emerging data center and cloud platforms for accelerating Homomorphic Encryption (HE) based DNNs for real-time secure applications. Successful completion of this project will lead to a significant increase in the capabilities of privacy sensitive applications by enabling them to utilize public clouds in a trusted and secure manner.

The project will identify and expose underrepresented and underserved students to STEM (Science, Technology, Engineering, Mathematics) through various programs at the University of Southern California. The proposed research will also constitute materials appropriate for inclusion in graduate and undergraduate courses.

All software developed in the project will be posted on github at: https://github.com/pgroupATusc. Software releases will be maintained for a period of not less than 3-years after the conclusion of the grant.

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.