Paper 2024/1433
: Making MPC-based Collaborative Analytics Efficient on Dynamic Databases
Abstract
Secure Multi-party Computation (MPC) provides a promising solution for privacy-preserving multi-source data analytics. However, existing MPC-based collaborative analytics systems (MCASs) have unsatisfying performance for scenarios with dynamic databases. Naively running an MCAS on a dynamic database would lead to significant redundant costs and raise performance concerns, due to the substantial duplicate contents between the pre-updating and post-updating databases.
In this paper, we propose
Metadata
- Available format(s)
-
PDF
- Category
- Applications
- Publication info
- Published elsewhere. Major revision. ACM SIGSAC Conference on Computer and Communications Security
- DOI
- 10.1145/3658644.3690314
- Keywords
- Secure multi-party computationDatabase analyticsData update
- Contact author(s)
-
zhoupz @ mail nankai edu cn
xiaojie guo @ mail nankai edu cn
chenpinzhi @ mail nankai edu cn
tongli @ nankai edu cn
lvsiyi @ nankai edu cn
liuzheli @ nankai edu cn - History
- 2024-09-14: approved
- 2024-09-13: received
- See all versions
- Short URL
- https://ia.cr/2024/1433
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2024/1433, author = {Peizhao Zhou and Xiaojie Guo and Pinzhi Chen and Tong Li and Siyi Lv and Zheli Liu}, title = {$Shortcut$: Making {MPC}-based Collaborative Analytics Efficient on Dynamic Databases}, howpublished = {Cryptology {ePrint} Archive, Paper 2024/1433}, year = {2024}, doi = {10.1145/3658644.3690314}, url = {https://eprint.iacr.org/2024/1433} }