STARSS: Small: GC@Scale: Synthesis, optimization, and implementation of Garbled Circuits for Scalable Privacy-Preserving Computing

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Computing on sensitive data is a standing challenge central to several modern-world applications. Secure Function Evaluation (SFE) allows mistrusting parties to jointly compute an arbitrary function on their private inputs without revealing anything but the result. The GC@Scale project focuses on novel scalable methods for addressing SFE, which directly translate to stronger cryptography and security for myriads of tasks with sensitive data. The applications are wide reaching and include privacy-preserving processing of medical, genome, and biometric data, as well as personal, government, and industrial cloud computing. The project includes an ambitious educational program that targets both undergraduate/ graduate students, and also addresses issues related to outreach.The concept of SFE using Garbled Circuits (GC) was introduced by Yao. Despite a decade of research in GC implementation and several key progresses, scalability of the available methods has been hampered by the circuit representation as a directed acyclic graph, and software-level local logic optimizations. GC@Scale leverages PI's recent work, which has changed the SFE landscape by viewing GC generation as an atypical sequential logic synthesis. The project plans to advance the understanding and enable expanded exploration of SFE methodologies, while simultaneously enriching the theory, practice, and tools for logic design, synthesis, mapping and optimization. The proposed plan includes: (i) design and FPGA implementation of an efficient general purpose Garbled Processor for secure computation; (ii) Creating the challenging application-specific GC matching and search engines with a higher than linear complexity. (iii) Devising new custom SFE engines for Machine Learning tasks.