Privacy-aware document retrieval with two-level inverted indexing

Previous work on privacy-aware ranking has addressed the minimization of information leakage when scoring top k documents, and has not studied on how to retrieve these top documents and their features for ranking. This paper proposes a privacy-aware document retrieval scheme with a two-level inverte...

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Bibliographic Details
Published in:Information retrieval (Boston) 2023-12, Vol.26 (1-2), p.12, Article 12
Main Authors: Qiao, Yifan, Ji, Shiyu, Wang, Changhai, Shao, Jinjin, Yang, Tao
Format: Article
Language:English
Subjects:
Computer Science
Data Mining and Knowledge Discovery
Data Structures and Information Theory
Documents
Efficiency
Information retrieval
Information Storage and Retrieval
Leakage
Natural Language Processing (NLP)
Pattern Recognition
Privacy
Queries
Query processing
Ranking
Ratings & rankings
Retrieval
Tags
Citations: Items that this one cites
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Summary:Previous work on privacy-aware ranking has addressed the minimization of information leakage when scoring top k documents, and has not studied on how to retrieve these top documents and their features for ranking. This paper proposes a privacy-aware document retrieval scheme with a two-level inverted index structure. In this scheme, posting records are grouped with bucket tags and runtime query processing produces query-specific tags in order to gather encoded features of matched documents with a privacy protection during index traversal. To thwart leakage-abuse attacks, our design minimizes the chance that a server processes unauthorized queries or identifies document sharing across posting lists through index inspection or across-query association. This paper presents the evaluation and analytic results of the proposed scheme to demonstrate the tradeoffs in its design considerations for privacy, efficiency, and relevance.
ISSN:1386-4564
1573-7659
DOI:10.1007/s10791-023-09428-z