STEED: Space and Time-efficient Encrypted Database Using FHE

In the era of Big Data, enterprises and individuals often upload databases to the cloud for storage and querying, which involves the risk of data leakage. Encrypted databases based on fully homomorphic encryption (FHE) theoretically solve the leakage problem, but the actual deployment of such encryp...

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Vydané v:IEEE transactions on dependable and secure computing s. 1 - 18
Hlavní autori: Zhang, Fahong, Gu, Zhen, Li, Yiran, Wang, Yilei, Hong, Cheng, Chen, Zhaohui, Wang, Yufei, Lu, Yanheng, Li, Meng, Liu, Leibo, Wang, Sheng, Li, Feifei, Yang, Chen, Niu, Dimin, Xie, Yuan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 2025
Predmet:
Computational efficiency
Costs
Data transfer
Encrypted databases
Homomorphic encryption
Noise
Polynomials
private query processing
Servers
Single instruction multiple data
Vectors
ISSN:1545-5971, 1941-0018
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Popis
Shrnutí:In the era of Big Data, enterprises and individuals often upload databases to the cloud for storage and querying, which involves the risk of data leakage. Encrypted databases based on fully homomorphic encryption (FHE) theoretically solve the leakage problem, but the actual deployment of such encrypted databases faces the challenge of high economic costs. Cloud service providers charge for data transfer volume and computation time. Unfortunately, FHE is very expensive in both aspects, with more than five orders of magnitude deterioration compared to directly transmitting and computing plaintext. In this paper, we present STEED, a low-cost encrypted database that tackles both bottlenecks simultaneously. In STEED, we first introduce a FHE framework called BatchPBS, a batch pro grammable bootstrapping framework that improves the recent Liu and Wang (ASIACRYPT 2023) amortised scheme from 6.7 ms to 3 msper ciphertext while adding multi-value bootstrapping (MVB) support. Based on BatchPBS, we propose efficient SQL algorithms in SIMD-style to reduce the computation time and a novel AES transcipher protocol to reduce the data transfer volume. Thus, STEED reduces query time by 13 × and data transfer amount by 165 to 534.9 × compared with SOTA work. Considering end-to-end economic cost of TPC-H query on a database with 1 million rows, STEED reduces the expense of deploying on AWS by 28444.8 per 100 queries. (The code can be found at https://github.com/alibaba-damo-academy/ctl-he)
ISSN:1545-5971
1941-0018
DOI:10.1109/TDSC.2025.3630273