Efficient Verifiable Dynamic Searchable Symmetric Encryption With Forward and Backward Security

In the realm of secure data outsourcing, verifiable dynamic searchable symmetric encryption (VDSSE) enables a client to verify search results obtained from an untrusted server while protecting the data privacy. Nevertheless, the storage cost of verification structure in some schemes escalates linearly with the number of keywords, and the generation of proofs demands a substantial number of exponentiation operations. Moreover, some schemes overlook forward and backward security in the dynamic database. In this article, we introduce FB-VDSSE, an advanced VDSSE scheme that ensures both forward and backward security. Specifically, we introduce an efficient accumulation commitment verification structure (AC-VS) that attains a commitment verification value with a constant-size storage cost. Based on the AC-VS, we further propose a forward and backward secure VDSSE scheme. Within this scheme, the server exclusively generates a membership proof at the corresponding index of the vector, reducing the computation cost associated with the search operation. Finally, we provide the security proof and functional comparison, demonstrating that our scheme effectively ensures forward security, backward security, and verifiability. Additionally, the experimental evaluations underscore the efficiency of our scheme, showcasing its superior performance compared to relevant schemes in practical scenarios.

Authors

Institutions

Publication Details

Journal
IEEE Internet of Things Journal
Published
2024-09-30
DOI
https://doi.org/10.1109/jiot.2024.3470772
Citations
9
Primary Topic
Cryptography and Data Security
Type
article
Field-Weighted Citation Impact
3.13

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Efficient Verifiable Dynamic Searchable Symmetric Encryption With Forward and Backward Security

Kun He, Chenbin Zhao, Ximeng Liu, Jiguo Li et al.
9 citations
IEEE Internet of Things Journal
Cryptography and Data Security
3.13
article

Efficient Verifiable Dynamic Searchable Symmetric Encryption With Forward and Backward Security

Kun He, Chenbin Zhao, Ximeng Liu, Jiguo Li, Jianting Ning, Jing Chen, Ruiying Du
article en
9 citations

Abstract

In the realm of secure data outsourcing, verifiable dynamic searchable symmetric encryption (VDSSE) enables a client to verify search results obtained from an untrusted server while protecting the data privacy. Nevertheless, the storage cost of verification structure in some schemes escalates linearly with the number of keywords, and the generation of proofs demands a substantial number of exponentiation operations. Moreover, some schemes overlook forward and backward security in the dynamic database. In this article, we introduce FB-VDSSE, an advanced VDSSE scheme that ensures both forward and backward security. Specifically, we introduce an efficient accumulation commitment verification structure (AC-VS) that attains a commitment verification value with a constant-size storage cost. Based on the AC-VS, we further propose a forward and backward secure VDSSE scheme. Within this scheme, the server exclusively generates a membership proof at the corresponding index of the vector, reducing the computation cost associated with the search operation. Finally, we provide the security proof and functional comparison, demonstrating that our scheme effectively ensures forward security, backward security, and verifiability. Additionally, the experimental evaluations underscore the efficiency of our scheme, showcasing its superior performance compared to relevant schemes in practical scenarios.

IEEE Internet of Things JournalVol. 12(3)
Fujian Normal University (CN), Wuhan University (CN), Fuzhou University (CN)
National Natural Science Foundation of China, Key Technology Research and Development Program of Shandong, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 7%
Cryptography and Data Security
3.13
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.