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Project Overview

As sensitive information is increasingly stored on 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 on 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 studies algorithmic indexing and information retrieval solutions for privacy-aware cloud document search.

People

Shiyu Ji (Collaborator at Google)
Yifan Qiao
Jinjin Shao
Yingrui Yang
Tao Yang

Publication and other project materials

Y. Yang, Y. Qiao, T. Yang, Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking, Proc .of ACL'22 (60th Annual Meeting of the Association for Computational Linguistics). Pages 695–707. arXiv version
Y. Yang, Y. Qiao, J. Shao, X. Yan, T. Yang, Lightweight Composite Re-Ranking for Efficient Keyword Search with BERT. arXiv:2103.06499. 2021. Proc. of WSDM'22 (ACM International Conference on Web Search and Data Mining).
J. Shao, Y. Qiao, S. Ji and T. Yang, Window Navigation with Adaptive Probing for Executing BlockMax WAND . Proc. of 2021 ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR 2021). Slides
J. Shao, S. Ji, A. O. Glova, Y. Qiao, T. Yang, and T. Sherwood. Index Obfuscation for Oblivious Document Retrieval in a Trusted Execution Environment. Proc. of 29th ACM International Conference on Information and Knowledge Management (CIKM 2020). Pages 1345–1354. Slides

Acknowledgment: This material is based upon work supported by the National Science Foundation 2040146 (2020-2022). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Last update: July 28, 2020

Privacy-aware Document Search on the Cloud