Projects per year
Abstract
One major concern of distributed computation in networks is the privacy of the individual nodes. To address this privacy issue in the context of the distributed average consensus problem, we propose a general, yet simple solution that achieves privacy using additive secret sharing, a tool from secure multiparty computation. This method enables each node to reach the consensus accurately and obtains perfect security at the same time. Unlike differential privacy based approaches, there is no trade-off between privacy and accuracy. Moreover, the proposed method is computationally simple compared to other techniques in secure multiparty computation, and it is able to achieve perfect security of any honest node as long as it has one honest neighbour under the honest-but-curious model, without any trusted third party.
Original language | English |
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Title of host publication | EUSIPCO 2019 - 27th European Signal Processing Conference |
Publisher | IEEE Signal Processing Society |
Publication date | Sept 2019 |
ISBN (Electronic) | 9789082797039 |
DOIs | |
Publication status | Published - Sept 2019 |
Event | 27th European Signal Processing Conference, EUSIPCO 2019 - Coruña, Spain Duration: 2 Sept 2019 → 6 Sept 2019 |
Conference
Conference | 27th European Signal Processing Conference, EUSIPCO 2019 |
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Country/Territory | Spain |
City | Coruña |
Period | 02/09/2019 → 06/09/2019 |
Series | Proceedings of the European Signal Processing Conference |
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ISSN | 2076-1465 |
Keywords
- Distributed average consensus
- additive secret sharing
- privacy preserving
- secure multiparty computation
Fingerprint
Dive into the research topics of 'Privacy-Preserving Distributed Average Consensus based on Additive Secret Sharing'. Together they form a unique fingerprint.Projects
- 1 Finished
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SECURE: Secure Estimation and Control Using Recursion and Encryption
Wisniewski, R., Christensen, M. G., Andersen, A. O., Mannov, A., Geil, O. & Jessen, J. F.
01/04/2018 → 30/11/2021
Project: Research
Research output
- 21 Citations
- 1 PhD thesis
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Privacy-Preserving Distributed Processing Over Networks
Qiongxiu, L., 2021, Aalborg Universitetsforlag. 188 p.Research output: PhD thesis
Open AccessFile305 Downloads (Pure)