EAGER: Efficient Privacy-aware Document Search in the Cloud

EAGER：云端高效的隐私意识文档搜索

• 批准号: 2040146
• 负责人: Tao Yang
• 金额: $ 21.75万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040146&HistoricalAwards=false
• 关键词: EAGER; Efficient; Privacy; aware; Document

As sensitive information is increasingly stored in the cloud, privacy protection is a critical factor for users to adopt cloud-based information services such as document search. A cloud server can observe the client-initiated query processing flow, extract statistical patterns, and reason about client's data. As a result, the risk of leakage-abuse attacks exists when searching in the cloud. The main challenge to perform privacy-preserving search is that index visitation can reveal sensitive data patterns, and computation involved in advanced ranking can further expose private feature information. On the other hand, hiding index and feature information through full encryption prevents the server from performing effective scoring and result comparison. This project explores the challenging open problems in algorithmic indexing and ranking solutions for privacy-aware cloud data search. The approach emphasizes an evaluation-driven design where search performance is assessed in multiple aspects of relevance, efficiency, and privacy for practical system deployment. The project integrates the proposed research with an educational plan including undergraduate and graduate students' involvement in the research project, instructional material development, and outreach activities.The exploratory research addresses two fundamental research challenges: (1) privacy-aware indexing and runtime support in matching documents for a given query with an emphasis to curtail statistical text information leakage while providing efficient and private access of ranking features; (2) privacy-aware end-to-end top-K ranking with a multi-stage scheme which seeks a combination of linear and nonlinear methods such as neural nets and learning ensembles. The design goal is to minimize the leakage of document features and characteristics while still accomplishing a reasonable response time and competitive relevance. The evaluation process will use public datasets to assess the effectiveness of the developed techniques for practical system deployment. This research effort will open the door for bridging the gap between privacy and advanced information retrieval in searching large encrypted datasets. The developed research results will be made public for research and industry communities.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.

随着敏感信息越来越多地存储在云中，隐私保护是用户采用文档搜索等基于云的信息服务的关键因素。云服务器可以观察客户端发起的查询处理流程，提取统计模式，并对客户端数据进行推理。因此，在云中搜索时存在泄漏滥用攻击的风险。执行隐私保护搜索的主要挑战是索引访问会揭示敏感数据模式，而高级排名所涉及的计算可能会进一步暴露隐私特征信息。另一方面，通过完全加密隐藏索引和特征信息会阻止服务器执行有效的评分和结果比较。该项目探索了隐私感知的云数据搜索的算法索引和排名解决方案中具有挑战性的公开问题。该方法强调评估驱动的设计，从相关性、效率和隐私等多个方面对搜索性能进行评估，以实现实际的系统部署。该项目将拟议的研究与教育计划相结合，包括本科生和研究生参与研究项目、教材开发和推广活动。探索性研究解决了两个基本研究挑战：(1)针对给定查询的文档匹配中的隐私意识索引和运行时支持，重点是减少统计文本信息的泄漏，同时提供对排名特征的高效和私人访问；(2)隐私感知的端到端Top-K排名和多阶段方案，该方案寻求线性和非线性方法的组合，如神经网络和学习集成。设计目标是在保持合理的响应时间和竞争相关性的同时，最大限度地减少文档特征和特征的泄漏。评价过程将使用公共数据集来评估所开发的技术在实际系统部署方面的有效性。这项研究工作将为在搜索大型加密数据集方面弥合隐私和高级信息检索之间的差距打开大门。开发的研究成果将向研究和行业团体公开。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Index Obfuscation for Oblivious Document Retrieval in a Trusted Execution Environment

• DOI: 10.1145/3340531.3412035
• 发表时间: 2020-10
• 期刊: Proceedings of the 29th ACM International Conference on Information & Knowledge Management
• 作者: Jinjin Shao;Shiyu Ji;A. O. Glova;Yifan Qiao;Tao Yang;T. Sherwood

Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking

具有上下文量化的紧凑令牌表示，可实现高效的文档重新排序

• DOI: 10.18653/v1/2022.acl-long.51
• 发表时间: 2022
• 期刊: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics
• 作者: Yang, Yingrui;Qiao, Yifan;Yang, Tao
• 通讯作者: Yang, Tao

Lightweight Composite Re-Ranking for Efficient Keyword Search with BERT

• DOI: 10.1145/3488560.3498495
• 发表时间: 2021-03
• 期刊: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
• 作者: Yingrui Yang;Yifan Qiao;Jinjin Shao;Xifeng Yan;Tao Yang

Window Navigation with Adaptive Probing for Executing BlockMax WAND

用于执行 BlockMax WAND 的带有自适应探测的窗口导航

• DOI: 10.1145/3404835.3463109
• 发表时间: 2021
• 期刊: Proc. of 2021 ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR 2021
• 作者: Shao, J.;Qiao, Y.;Ji, S.;Yang, T.
• 通讯作者: Yang, T.