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

客户通过使用托管强大的 ML 模型的云服务器不断增长的智能数据处理需求，极大地推动了机器学习 (ML) 作为一项服务。尽管普遍存在，外包机器学习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，客户端要么需要与服务器共享他们的敏感数据，例如医疗记录、财务信息，要么服务器必须向客户端公开模型。为了保证隐私，同态加密 (HE)、多方计算 (MPC) 等加密协议的兴起使得机器学习能够直接对加密数据进行分析。虽然很诱人，但理论和实践之间仍然存在很大差距，例如，由于密文上极其昂贵的计算或通信开销而导致长延迟。该项目旨在通过提供高效、可扩展和加密意识计算范例的全面开发来切实加速私有机器学习服务。该项目的新颖之处在于新的 ML 特定加密运算符、保留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。该项目的更广泛意义和重要性是：（1）推进可信人工智能（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，通过里哈伊大学、东北大学和康涅狄格大学的相关项目培养来自不同背景的学生。该项目将开发高效、可扩展和实用的算法-硬件协同优化解决方案的多方面设计范式，以显着加速FPGA等硬件平台上的隐私保护机器学习。该项目由三个干预研究重点组成：（1）协调加密域中的信息表示和模型稀疏性，从根本上减少 HE 推理中的内存和计算占用； (2) 通过加密感知模型截断和部分硬件重新配置等技术，克服基于 MPC 的解决方案带来的超高开销； （3）通过联合优化非线性运算缩减和闭环“算法-硬件”设计空间探索，寻找加密友好且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智力价值和更广泛的影响审查标准进行评估，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.