Collaborative Research: SaTC: CORE: Medium: Accelerating Privacy-Preserving Machine Learning as a Service: From Algorithm to Hardware

机器学习（ML）作为一种服务，正在通过使用云服务器（其中托管了强大的ML模型）来满足客户不断增长的智能数据处理需求。虽然普遍存在，但外包的ML处理对个人或企业提供商的数据隐私构成了真实的威胁。例如，客户端需要与服务器共享其敏感数据，例如医疗记录、财务信息，或者服务器必须向客户端公开模型。为了保证隐私，加密协议的兴起，如同态加密（HE），多方计算（MPC），使ML分析直接对加密数据。虽然诱人，但理论与实践之间仍存在很大差距，例如，由于在密文上过于昂贵的计算或通信开销而导致的长等待时间。该项目旨在通过提供高效，可扩展和预防意识计算范例的全面开发来实际加速私有ML服务。该项目的新颖之处在于新的ML特定的加密运算符，保持准确性和加密友好的神经架构，以及开创性的算法硬件协同设计方法。该项目更广泛的意义和重要性是：（1）推进值得信赖的人工智能（AI），这是国家人工智能计划的国家战略支柱之一;（2）加深对密码学，机器学习和硬件加速之间相互作用的理解;（3）丰富计算机工程课程，以及通过利哈伊大学、东北大学和康涅狄格大学的相关项目对来自不同背景的学生进行培训。该项目将开发一个多方面的设计范例，可扩展且实用的算法-硬件协同优化解决方案，可显著加速FPGA等硬件平台上的隐私保护机器学习。该项目包括三个研究方向：（1）在加密域中协调信息表示和模型稀疏性，以从根本上减少HE推理中的内存和计算占用;（2）通过防御感知模型截断和部分硬件重构等技术，克服与基于MPC的解决方案相关的超高开销;（3）通过联合优化非线性运算简化和闭环“算法-硬件”设计空间探索，寻找加密友好和保持准确性的神经架构。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Machine learning (ML) as a service is being overwhelmingly driven by the ever-increasing clients' intelligent data processing needs through the use of cloud servers, where powerful ML models are hosted. Although pervasive, out-sourced ML processing poses real threats to personal or business providers' data privacy. For example, the clients either need to share their sensitive data, such as healthcare records, financial information, with the server, or the server has to disclose the model to the clients. To guarantee privacy, the rise of cryptographic protocols, such as Homomorphic Encryption (HE), Multi-Party Computation (MPC), enable ML analytics directly on the encrypted data. While enticing, there still exists a big gap between the theory and practice, e.g., long latency due to the prohibitively expensive computation or communication overhead over ciphertext. This project aims to practically accelerate the private ML service by offering a full-fledged development of efficient, scalable and encryption-conscious computing paradigms. The project's novelties lie in new ML-specific cryptographic operators, accuracy-preserving and crypto-friendly neural architectures, and pioneered algorithm-hardware co-design methodologies. The project's broader significance and importance are: (1) to advance trustworthy artificial intelligence (AI), one of the national strategic pillars of the National AI Initiative; (2) to deepen the understanding of interactions among cryptography, machine learning and hardware acceleration; (3) to enrich the computer engineering curriculum, and the training of students from diverse backgrounds through relevant programs at Lehigh University, Northeastern University, and the University of Connecticut.The project will develop a multifaceted design paradigm for efficient, scalable and practical algorithm-hardware co-optimized solutions to significantly accelerate privacy-preserving machine learning on hardware platforms such as FPGA. This project consists of three intervening research thrusts: (1) to orchestrate information representation and model sparsity in the encryption domain to fundamentally decrease the memory and computation footprint in the HE inference; (2) to overcome the ultra-high overhead associated with the MPC-based solution through techniques such as encryption-aware model truncation and partial hardware reconfiguration; (3) to search for crypto-friendly and accuracy-preserving neural architectures via jointly optimizing non-linear operation reduction, and closed loop "algorithm-hardware" design space exploration.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.