On-Device Query Auto-Completion for Email Search
AuthorsYifan Qiao, Otto Godwin, Hua Ouyang
AuthorsYifan Qiao, Otto Godwin, Hua Ouyang
Traditional query auto-completion (QAC) relies heavily on search logs collected over many users. However, in on-device email search, the scarcity of logs and the governing privacy constraints make QAC a challenging task. In this work, we propose an on-device QAC method that runs directly on users’ devices, where users’ sensitive data and interaction logs are not collected, shared, or aggregated through web services. This method retrieves candidates from pseudo relevance feedback, and ranks them based on relevance signals that explore the textual and structural information from users’ emails. We also propose a private corpora based evaluation method, and empirically demonstrate the effectiveness of our proposed method.
Understanding how people use their devices often helps in improving the user experience. However, accessing the data that provides such insights — for example, what users type on their keyboards and the websites they visit — can compromise user privacy. We develop a system architecture that enables learning at scale by leveraging local differential privacy, combined with existing privacy best practices. We design efficient and scalable local differentially private algorithms and provide rigorous analyses to demonstrate the tradeoffs among utility, privacy, server computation, and device bandwidth. Understanding the balance among these factors leads us to a successful practical deployment using local differential privacy. This deployment scales to hundreds of millions of users across a variety of use cases, such as identifying popular emojis, popular health data types, and media playback preferences in Safari. We provide additional details about our system in the full version.