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，通过使用托管强大ML模型的云服务器，正在压倒性地受到不断增长的客户智能数据处理需求的推动。尽管ML处理无处不在，但外包ML处理对个人或企业提供商的数据隐私构成了真正的威胁。例如，客户端需要与服务器共享他们的敏感数据，如医疗记录、财务信息，或者服务器必须向客户端披露模型。为了确保隐私，同态加密(HE)、多方计算(MPC)等密码协议的兴起使ML能够直接对加密数据进行分析。在诱人的同时，理论和实践之间仍然存在着很大的差距，例如，由于密文的计算或通信开销高得令人望而却步而导致的长延迟。该项目旨在通过提供高效、可扩展和加密敏感的计算范例的全面开发，实际上加速私有ML服务。该项目的新颖性在于新的特定于ML的密码运算符、保持精度和密码友好的神经体系结构，以及开创性的算法-硬件联合设计方法。该项目的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AI倡议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)通过利哈伊大学、东北大学和康涅狄格大学的相关项目，丰富计算机工程课程，培养来自不同背景的学生。该项目将为高效、可扩展和实用的算法-硬件联合优化解决方案开发多方面的设计范式，以显著加快在硬件平台(如FPGA)上保护隐私的机器学习。该项目包括三个中间研究推力：(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足迹；(2)通过加密感知模型截断和部分硬件重构等技术来克服基于MPC的解决方案的超高开销；(3)通过联合优化非线性运算简化和闭环系统“算法-硬件”设计空间探索，寻找密码友好和保持精度的神经体系结构。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.