Collaborative Research: CCRI: New: Medium: A Development and Experimental Environment for Privacy-preserving and Secure (DEEPSECURE) Machine Learning

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While machine learning (ML) is embraced as an important tool for various science, engineering, medical, finance, and homeland security applications, it is becoming an increasingly attractive target for cybercriminals. DEEPSECURE is a first-of-its-kind development and experimental platform to support secure and privacy-preserving ML research. With its novel modular design integrated with fully customizable function blocks and sample modules, DEEPSECURE is a game-changing tool to effectively support research in this emerging field by enabling fast design, prototyping, evaluation, and re-innovation of trust-worthy ML applications. It enables a variety of compelling new research projects that focus on ML security and privacy, leading to breakthroughs to protect ML systems and accelerating their development and widening their adoption. It will contribute significantly to the protection of the future cyber and physical world and safeguard human society. DEEPSECURE receives strong community support from over 20 key stakeholders across the country. The project includes significant efforts for fostering and sustaining an ML security and privacy research community, including monthly virtual open forums to provide a regular update to and seek feedback from the community, quarterly advisory board meetings, annual symposiums, and a training workshop series. The project includes specific measures and plans for inspiring the participation of underrepresented groups and infusing diversity and inclusion in all DEEPSECURE events and activities. The project output includes an open-source and easy-to-use learning platform for curriculum development and workforce training. To support building a sustainable workforce development pipeline, the project team participates in the existing annual GenCyber summer camps for K-12 students and a Cyber Saturday series to introduce cybersecurity and AI career paths and educational resources to K-12 school counselors, teachers, students, and parents.Recent development in privacy-preserving and secure ML draws expertise from both ML and security/privacy to tackle the multi-faceted problem. However, the research community is facing fundamental challenges in this emerging area due to its interdisciplinary nature. On the one hand, although deep learning frameworks such as Pytorch and Tensorflow have been made widely available, a critical hurdle faced by ML researchers is the steep learning curve to effectively use security techniques and libraries to tackle ML security and privacy problems. On the other hand, while the security community has developed highly efficient cryptographic libraries, it remains nontrivial to integrate them into deep learning models to achieve a computation efficiency suited for practical applications. The overarching goal of the project is to close the gap by developing DEEPSECURE, which integrates a spectrum of essential functions and building blocks that are ready-to-use to flatten the learning curve for researchers coming from both ML and security/privacy communities. At the same time, DEEPSECURE is fully customizable and scalable, enabling deep and fundamental research toward privacy-preserving and secure ML. To meet the overarching goal, specific project objectives include: (1) acquiring a scalable and re-configurable compute environment based on the latest Dell, AMD, and Nvidia technologies to establish the DEEPSECURE hardware infrastructure across the campuses of Old Dominion University and University of Buffalo; (2) developing a new software platform to support DEEPSECURE SDE (Software Development Environment) and MEC (Multi-user Experimental Chamber). The platform is integrated with PyTorch to enable great usability for both beginners and advanced researchers and feature a scalable and customizable modular framework with seamlessly integrated libraries, function blocks, and sample modules; (3) promoting DEEPSECURE across the nation to ensure broad participation, collaboration, and sharing; (4) leveraging DEEPSECURE to foster a long-lasting, self-sustainable ML security and privacy research community that engages all stakeholders in a sustained and ongoing way; and last but not least, (5) educating and training diverse cybersecurity workforce to safeguard the future intelligent cyber systems.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.