LAORAM：面向大型嵌入表训练的ORAM体系结构展望
LAORAM: A Look Ahead ORAM Architecture for Training Large Embedding Tables
摘要：
数据机密性正成为一个重要的问题，特别是在云计算时代。内存访问模式已经被证明会泄漏关键信息，如安全密钥和程序的空间和时间信息。这种信息泄漏在具有嵌入表的机器学习模型中构成了更大的隐私挑战。嵌入表通常用于从训练数据中学习分类特征。即使知道访问的嵌入表条目的位置，而不是嵌入表中的数据，也会损害模型的分类输入数据。嵌入条目是隐私敏感的，因为它们公开了有关用户的有价值的属性。不经意RAM（ORAM）及其增强型变体（如PathORAM）已成为隐藏内存访问流泄漏的可行解决方案。在这项工作中，我们提出了LAORAM，一个ORAM框架，明确设计用于在嵌入表训练过程中保护用户隐私。LAORAM利用了训练的独特性质，未来使用的训练样本是预先知道的。LAORAM对训练样本进行预处理，以识别在不久的将来一起访问的内存块。系统尝试将这些块分配给PathORAM基础结构中尽可能少的路径。LAORAM通过将一起访问的多个块组合为超级块来执行此操作。为了进一步提高性能，LAORAM为PathORAM使用胖树结构，减少了所需的后台驱逐次数，从而提高了存储使用率。我们已经评估了LAORAM使用推荐模型（DLRM）和NLP模型（XLM-R）嵌入表配置。LAORAM在推荐数据集（Kaggle）上的执行速度比PathORAM快5倍，在NLP数据集（XNLI）上快5.4倍，同时保证与原始PathORAM相同的安全性。
英文摘要：
Data confidentiality is becoming a significant concern, especially in the cloud computing era. Memory access patterns have been demonstrated to leak critical information such as security keys and a program's spatial and temporal information. This information leak poses an even more significant privacy challenge in machine learning models with embedding tables. Embedding tables are routinely used to learn categorical features from training data. Even knowing the locations of the embedding table entries accessed, not the data within the embedding table, will compromise categorical input data to the model. Embedding entries are privacy-sensitive since they disclose valuable properties about the user. Oblivious RAM (ORAM), and its enhanced variants such as PathORAM have emerged as viable solutions to hide leakage from memory access streams. In this work, we present LAORAM, an ORAM framework explicitly designed to protect user privacy during embedding table training. LAORAM exploits the unique property of training, the training samples used in the future are known beforehand. LAORAM preprocesses the training samples to identify the memory blocks which are accessed together in the near future. The system tries to assign these blocks to as few paths as possible within the PathORAM infrastructure. LAORAM does this operation by combining multiple blocks accessed together as superblocks. To further increase performance, LAORAM uses a fat-tree structure for PathORAM reducing the number of background evictions required, which improves the stash usage. We have evaluated LAORAM using both a recommendation model (DLRM) and a NLP model (XLM-R) embedding table configurations. LAORAM performs 5 times faster than PathORAM on a recommendation dataset (Kaggle) and 5.4x faster on a NLP dataset (XNLI), while guaranteeing the same security guarantees as the original PathORAM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
