Paper 2024/1756
: Secure Graph Computation Made More Scalable
Abstract
Privacy-preserving graph analysis allows performing computations on graphs that store sensitive information while ensuring all the information about the topology of the graph, as well as data associated with the nodes and edges, remains hidden. The current work addresses this problem by designing a highly scalable framework,
Metadata
- Available format(s)
-
PDF
- Category
- Applications
- Publication info
- Published elsewhere. Minor revision. ACM CCS 2024
- DOI
- 10.1145/3658644.3670393
- Keywords
- Secure graph analysissecure computationsecure shuffle
- Contact author(s)
-
koti @ encrypto cs tu-darmstadt de
varshabhat15 @ gmail com
arpita @ iisc ac in
bhavishraj @ iisc ac in - History
- 2024-10-30: approved
- 2024-10-28: received
- See all versions
- Short URL
- https://ia.cr/2024/1756
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2024/1756, author = {Nishat Koti and Varsha Bhat Kukkala and Arpita Patra and Bhavish Raj Gopal}, title = {$\mathsf{Graphiti}$: Secure Graph Computation Made More Scalable}, howpublished = {Cryptology {ePrint} Archive, Paper 2024/1756}, year = {2024}, doi = {10.1145/3658644.3670393}, url = {https://eprint.iacr.org/2024/1756} }