Privacy-Preserving Distributed Graph Filtering

Privacy-Preserving Distributed Graph Filtering

Li, Qiongxiu (Aalborg University)
Coutino, Mario (TU Delft Signal Processing Systems)
Leus, G.J.T. (TU Delft Signal Processing Systems)
Christensen, M. Graesboll (Aalborg University)

2020-08-01

With an increasingly interconnected and digitized world, distributed signal processing and graph signal processing have been proposed to process its big amount of data. However, privacy has become one of the biggest challenges holding back the widespread adoption of these tools for processing sensitive data. As a step towards a solution, we demonstrate the privacypreserving capabilities of variants of the so-called distributed graph filters. Such implementations allow each node to compute a desired linear transformation of the networked data while protecting its own private data. In particular, the proposed approach eliminates the risk of possible privacy abuse by ensuring that the private data is only available to its owner. Moreover, it preserves the distributed implementation and keeps the same communication and computational cost as its non-secure counterparts. Furthermore, we show that this computational model is secure under both passive and eavesdropping adversary models. Finally, its performance is demonstrated by numerical tests and it is shown to be a valid and competitive privacypreserving alternative to traditional distributed optimization techniques.

Distributed computation
Distributed graph filters
Encryption
Graph signal processing
Privacy-preserving

http://resolver.tudelft.nl/uuid:ac3d9a76-59bb-4c77-ae81-f75a2813ab5d

https://doi.org/10.23919/Eusipco47968.2020.9287429

Eurasip, Amsterdam (Netherlands)

2021-08-29

978-9-0827-9705-3

28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings

EUSIPCO 2020, 2021-01-18 → 2021-01-22, Amsterdam, Netherlands

European Signal Processing Conference, 2219-5491, 2021-January

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2020 Qiongxiu Li, Mario Coutino, G.J.T. Leus, M. Graesboll Christensen