Secure Join and Compute in Encrypted Database

In public and shared platforms where security is paramount, encrypted database storage and processing have become a key research priority. However, traditionally encrypted databases do not support direct computation of relational queries, and hence, those types of query processing are infeasible wit...

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Bibliographic Details
Published in:IEEE ... International Conference on Trust, Security and Privacy in Computing and Communications (Online) pp. 1480 - 1485
Main Authors: Parbat, Tanusree, Chatterjee, Ayantika
Format: Conference Proceeding
Language:English
Published: IEEE 17.12.2024
Subjects:
Cryptography
Data privacy
Hardware
Homomorphic Encrypted Database
Homomorphic encryption
Multi-party computation
Personal protective equipment
Query processing
SQL Join
ISSN:2324-9013
Online Access:Get full text
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Summary:In public and shared platforms where security is paramount, encrypted database storage and processing have become a key research priority. However, traditionally encrypted databases do not support direct computation of relational queries, and hence, those types of query processing are infeasible without intermediate decryption. To achieve end-to-end encrypted computation, such databases prefer fully homomorphic encryption (FHE), which demands circuit-based representation of any algorithm. In our work, along with other encrypted SQL operations, we analyze SQL join and show straightforward implementation, which incurs huge performance overhead. Not only in performance, number of ciphertext expansions is also huge in this case, which can trigger a sharp rise in performance overhead in encrypted domain. To address these challenges, we propose a lightweight solution only for joining the encrypted databases using pair-wise traditional column encryption with the introduction of primary and foreign keys. Then, we highlight how to perform homomorphic operations over that encrypted joining result. Our implementation shows 89.34% improvement compared to straightforward FHE-joining. Consequently, we demonstrate memory overhead reduction ∼13%.
ISSN:2324-9013
DOI:10.1109/TrustCom63139.2024.00204