EAGER: Efficient Privacy-aware Document Search in the Cloud

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基于云的信息服务（如文档搜索）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，并对客户端的数据进行推理。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可以揭示敏感的数据模式，并且高级排名中涉及的计算可以进一步暴露私有特征信息。 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。该项目探讨了隐私感知云数据搜索的算法索引和排名解决方案中具有挑战性的开放问题。该方法强调了评估驱动的设计，搜索性能评估的多个方面的相关性，效率和隐私的实际系统部署。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的研究挑战：（1）私隐─在为给定查询匹配文档时的感知索引和运行时支持，重点是减少统计文本信息泄漏，同时提供高效和私密的访问排名特征;（2）具有多阶段方案的隐私感知端到端top-K排名，该方案寻求线性和非线性方法的组合，例如神经网络和学习集成。设计目标是最小化文档特性和特征的泄漏，同时仍然实现合理的响应时间和竞争相关性。评估过程将使用公共数据集来评估所开发的实用系统部署技术的有效性。 这项研究工作将为弥合隐私和高级信息检索在搜索大型加密数据集方面的差距打开大门。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。

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.