基于深度学习的推荐系统中稀疏特征访问模式导致的数据泄漏
Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems
摘要：
在线个性化推荐服务通常托管在云中，其中用户查询基于云的模型以接收推荐输入，例如感兴趣的商品或新闻提要。最先进的推荐模型依赖于稀疏和密集的特征来表示用户的个人资料信息和他们交互的项目。尽管稀疏特征占模型总规模的99%，但稀疏特征可能带来的信息泄漏问题尚未引起足够的重视。这些稀疏特征用于跟踪用户的行为，例如，他们的点击历史，对象交互等，潜在地携带每个用户的私人信息。稀疏特征表示为存储在大表中的学习嵌入向量，并且通过使用特定用户的稀疏特征来索引表来执行个性化推荐。即使使用最近提出的隐藏云中发生的计算的方法，云中的攻击者仍然能够跟踪对嵌入表的访问模式。本文探讨了私人信息，可以通过跟踪推荐模型的稀疏特征访问模式。我们首先描述了可以在不受信任的云中对推荐模型中的稀疏特征进行的攻击类型，然后演示了这些攻击中的每一种如何导致提取用户的私人信息或跟踪用户的行为。
英文摘要：
Online personalized recommendation services are generally hosted in the cloud where users query the cloud-based model to receive recommended input such as merchandise of interest or news feed. State-of-the-art recommendation models rely on sparse and dense features to represent users' profile information and the items they interact with. Although sparse features account for 99% of the total model size, there was not enough attention paid to the potential information leakage through sparse features. These sparse features are employed to track users' behavior, e.g., their click history, object interactions, etc., potentially carrying each user's private information. Sparse features are represented as learned embedding vectors that are stored in large tables, and personalized recommendation is performed by using a specific user's sparse feature to index through the tables. Even with recently-proposed methods that hides the computation happening in the cloud, an attacker in the cloud may be able to still track the access patterns to the embedding tables. This paper explores the private information that may be learned by tracking a recommendation model's sparse feature access patterns. We first characterize the types of attacks that can be carried out on sparse features in recommendation models in an untrusted cloud, followed by a demonstration of how each of these attacks leads to extracting users' private information or tracking users by their behavior over time.
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。
