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ShieldDB: An Encrypted Document Database With Padding Countermeasures

delete2023-04-01
delete6
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OA
AI
V
Viet Vo
X
Xingliang Yuan *
S
Shi-Feng Sun
J
Joseph K. Liu
‪
‪Surya Nepal‬
Cong WANG 封面图
Cong WANG (Cong Wang)
DOI:10.1109/TKDE.2021.3126607delete
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摘要

摘要

En 中文
Cloud storage systems have seen a growing number of clients due to the fact that more and more businesses and governments are shifting away from in-house data servers and seeking cost-effective and ease-of-access solutions. However, the security of cloud storage is underestimated in current practice, which resulted in many large-scale data breaches. To change the status quo, this paper presents the design of ShieldDB, an encrypted document database. ShieldDB adapts the searchable encryption technique to preserve the search functionality over encrypted documents without having much impact on its scalability. However, merely realising such a theoretical primitive suffers from real-world threats, where a knowledgeable adversary can exploit the leakage (aka access pattern to the database) to break the claimed protection on data confidentiality. To address this challenge in practical deployment, ShieldDB is designed with tailored padding countermeasures. Unlike prior works, we target a more realistic adversarial model, where the database gets updated continuously, and the adversary can monitor it at an (or multiple) arbitrary time interval(s). ShieldDB's padding strategies ensure that the access pattern to the database is obfuscated all the time. We present a full-fledged implementation of ShieldDB and conduct intensive evaluations on Azure Cloud.
Keyword:
Cryptography
Databases
Servers
Indexes
Encryption
Cloud computing
Privacy
Data security and privacy
management and querying of encrypted data
padding strategies

期刊

IEEE Transactions on Knowledge and Data Engineering 封面图
IEEE Transactions on Knowledge and Data Engineering
IF:
10.4
论文数:
6.9K
被引数:
3.2W

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Monash University
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5.4W
论文数: 5.4W
被引数: 79
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shanghai jiao tong university
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15.7W
论文数: 11.7W
被引数: 159
C
City University of Hong Kong
学者数:
2.3W
论文数: 3.0W
被引数: 6.1W
C
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