SHF: Small: Hardware-Software Co-design for Privacy Protection on Deep Learning-based Recommendation Systems

深度学习推荐模型（DLRM）在行业中被广泛采用，因为它们利用深度学习神经网络提供个性化推荐，从而显着提高用户体验和客户忠诚度。大型DLRM的大小主要由其嵌入表决定，嵌入表可能为10 GB甚至100 GB，因此将此类表保存在云服务器中是一个有吸引力的选择。不幸的是，查询云服务器上的嵌入表需要发送客户端的私有数据，因此可能会泄露他们的敏感信息。在云中部署大型DLRM时，隐私问题已成为一个主要挑战。采用传统的基于树的不经意存储协议可以提供高度鲁棒的隐私保护，但遭受显着的性能下降和低的存储空间利用率。该项目的重点是开发一个软硬件协同设计框架，以解决基于云的DLRM隐私保护的性能和空间问题。该项目的成果将通过在云中安全地提供个性化建议而产生重大的社会影响。研究人员将积极招募和培训来自弱势群体的本科生和研究生，并为K-12学生提供研究和教育机会。该项目围绕三个创新方法展开：（1）硬件-软件协同设计，以显著提高性能;（2）有效集成非易失性存储器，以显著提高存储空间利用率;（3）硬件-软件协同设计，以显著提高存储空间利用率。（3）对DLRM访问行为的研究，为安全协议设计提供了依据。特别是，该项目利用现代处理器上的片上可信计算硬件和DLRM的独特特性，使安全操作可以在客户端和可信处理器之间进行分区，以低开销实现有效的隐私保护。该项目包含为访问一个云服务器的一个安全用户组以及访问多个云服务器的多个安全用户组开发方案的具体步骤。成功解决隐私保护的性能和空间问题可以推进人工智能时代的现代计算范式，即，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。

Deep-learning recommendation models (DLRMs) are widely adopted in industries as they exploit deep-learning neural networks to provide personalized recommendations, which significantly enhance user experience and customer loyalty. The sizes of large DLRMs are dominated by their embedding tables, which may be of 10s or even 100s of gigabytes, making it an appealing choice to save such tables in cloud servers. Unfortunately, querying the embedding tables on cloud servers demands sending in clients' private data and thus may leak their sensitive information. The privacy issue has become a major challenge when deploying large DLRMs in the cloud. Adopting traditional tree-based oblivious memory protocols can provide highly robust privacy protection, but suffer from significant performance degradation and low memory space utilization. This project focuses on developing a hardware-software co-design framework to address the performance and space issues of privacy protection for cloud-based DLRMs. The project’s outcomes will have significant societal impact by servicing personalized recommendations securely in the clouds. The investigators will actively recruit and train undergraduate and graduate students from underrepresented groups, and provide research and education opportunities for K-12 students.The project centers around three innovative approaches: (1) A hardware-software co-design for significant performance improvement; (2) The effective integration of non-volatile memory for significant memory space utilization improvement; (3) The exploration of distinct DLRM access behaviors for secure protocol designs. In particular, the project exploits the on-chip trusted computing hardware on modern processors and the distinguishing characteristics of DLRMs such that secure operations can be partitioned among clients and trusted processors for effective privacy protection with low overhead. The project contains concrete steps to develop schemes for one security group of users accessing one cloud server as well as for multiple security groups of users accessing multiple cloud servers. Successfully addressing the performance and space issues of privacy protection can advance the modern computing paradigm in the AI era, i.e., how the large DLRMs as well as modern AI models are deployed.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.