Abstract
The fast-growing networked computing devices create many distributed systems and generate new signals on a large scale. Typical applications include peer-to-peer streaming of multimedia data, crowdsourcing, and measurement by sensor networks. Therefore, the massive amount of networked data is a form of big data, calling for new data structures and algorithms different from classical ones suitable for small data sizes. We consider a vital data format for recording information from networked distributed systems: signals on graphs. A significant concern is to protect the privacy of large scales of signals when processed at third parties, such as cloud data centers. A de-facto solution is to outsource encrypted data before they arrive at the third-parties. We propose a novel and efficient privacy-protected outsourced denoising algorithm based on the information-theoretic secure multi-party computation (secure MPC). Among the operations of signals on graphs, denoising is useful before further meaningful processing can occur. We experiment with our algorithms in a popular platform of secure MPC and compare it with Paillier's homomorphic encryption approach. The results demonstrate a better efficiency of our approach.
Original language | English |
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Title of host publication | 2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings |
ISBN (Electronic) | 9781728192017 |
DOIs | |
State | Published - 2021 |
Event | 53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Daegu, Korea, Republic of Duration: May 22 2021 → May 28 2021 |
Publication series
Name | Proceedings - IEEE International Symposium on Circuits and Systems |
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Volume | 2021-May |
ISSN (Print) | 0271-4310 |
Conference
Conference | 53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021 |
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Country/Territory | Korea, Republic of |
City | Daegu |
Period | 5/22/21 → 5/28/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE
ASJC Scopus subject areas
- Electrical and Electronic Engineering