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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| Vydáno v: | IEEE ... International Conference on Trust, Security and Privacy in Computing and Communications (Online) s. 1480 - 1485 |
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| Hlavní autoři: | , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
17.12.2024
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| Témata: | |
| ISSN: | 2324-9013 |
| On-line přístup: | Získat plný text |
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| Abstract | 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%. |
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| AbstractList | 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%. |
| Author | Chatterjee, Ayantika Parbat, Tanusree |
| Author_xml | – sequence: 1 givenname: Tanusree surname: Parbat fullname: Parbat, Tanusree email: tanusree.parbat@gmail.com organization: Indian Institute of Technology,Advanced Technology Development Centre,Kharagpur,India – sequence: 2 givenname: Ayantika surname: Chatterjee fullname: Chatterjee, Ayantika email: cayantika@gmail.com organization: Indian Institute of Technology,Advanced Technology Development Centre,Kharagpur,India |
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| Snippet | In public and shared platforms where security is paramount, encrypted database storage and processing have become a key research priority. However,... |
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| SubjectTerms | Cryptography Data privacy Hardware Homomorphic Encrypted Database Homomorphic encryption Multi-party computation Personal protective equipment Query processing SQL Join |
| Title | Secure Join and Compute in Encrypted Database |
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