Dynamic and Verifiable Fuzzy Keyword Search With Forward Security in Cloud Environments

作     者：Cui, Ningning Wang, Zhihao Pei, Lili Wang, Mengxiang Wang, Dong Cui, Jie Zhong, Hong 

作者机构：Anhui Univ Sch Comp Sci Hefei 230093 Peoples R China Changan Univ Sch Data Sci & Artificial Intelligence Xian 710064 Peoples R China China Natl Inst Standardizat Div Serv Standardizat Beijing 100181 Peoples R China 

出 版 物：《IEEE INTERNET OF THINGS JOURNAL》 (IEEE Internet Things J.)

年 卷 期：2025年第12卷第9期

页      面：11482-11494页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Innovation Fund for Chinese Universities-New Generation Information Technology Innovation Project [2023IT080] Basic Scientific Research Funds of Central Universities [300102404101, 300102404901] National Natural Science Foundation of China [U23A20308, 62202008, 62372002] 

主　　题：Indexes Keyword search Privacy Encryption Servers Data privacy Internet of Things Cloud computing Vectors Outsourcing Dynamic searchable encryption (SE) fuzzy keyword search forward privacy verifiable search 

摘      要：Dynamic searchable symmetric encryption (DSSE) ensures that outsourced data can be searched and updated without compromising data availability. Recent efforts on DSSE have mainly focused on exact keyword retrieval, but considering that misspellings are common and practical, it is necessary to support the functionality of fuzzy keyword search. However, most existing fuzzy keyword search schemes do not consider malicious cloud servers and forward-privacy guarantees. The former may lead to the implementation of a fraction of search operations or forge the results and the latter may reveal the association between the newly updated data and previous search tokens. Therefore, in this article, we investigate the issue of dynamic and verifiable fuzzy keyword search with forward security (DVFKF). Specifically, DVFKF first uses locality sensitive hash to map approximate keywords into the same hash bucket, then links the hash buckets to obtain the bucket strings. Next, to accelerate the performance and support forward privacy, we propose a chain index for each bucket string through counters. Further, to guarantee the integrity of the results, we integrate the Merkle hash tree and chain index to verify the correctness and completeness of the results. Finally, we conduct formal security analysis and empirical evaluations to demonstrate the feasibility and practicality of our scheme on real datasets.