| Title | Denoising Signals on the Graph for Distributed Systems by Secure Outsourced Computation |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Wang, Zhaohong, Guo, Jing |
| Conference Name | 2021 IEEE 7th World Forum on Internet of Things (WF-IoT) |
| Date Published | jun |
| Keywords | cloud computing, Computational efficiency, data structures, Distributed databases, Human Behavior, Metrics, noise reduction, outsourced database security, Protocols, pubcrawl, resilience, Resiliency, Scalability, Streaming media |
| Abstract | The burgeoning networked computing devices create many distributed systems and generate new signals on a large scale. Many Internet of Things (IoT) applications, such as peer-to-peer streaming of multimedia data, crowdsourcing, and measurement by sensor networks, can be modeled as a form of big data. Processing massive data calls for new data structures and algorithms different from traditional ones designed for small-scale problems. For measurement from networked distributed systems, we consider an essential data format: signals on graphs. Due to limited computing resources, the sensor nodes in the distributed systems may outsource the computing tasks to third parties, such as cloud platforms, arising a severe concern on data privacy. A de-facto solution is to have third parties only process encrypted data. We propose a novel and efficient privacy-preserving secure outsourced computation protocol for denoising signals on the graph based on the information-theoretic secure multi-party computation (ITS-MPC). Denoising the data makes paths for further meaningful data processing. From experimenting with our algorithms in a testbed, the results indicate a better efficiency of our approach than a counterpart approach with computational security. |
| DOI | 10.1109/WF-IoT51360.2021.9595245 |
| Citation Key | wang_denoising_2021 |
Denoising Signals on the Graph for Distributed Systems by Secure Outsourced Computation