Privacy-Preserving Distributed Graph Filtering

Qiongxiu Li, M. Coutino, G. Leus, M. Graesboll Christensen

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
7 Downloads (Pure)


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.
Original languageEnglish
Title of host publication28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
Place of PublicationAmsterdam (Netherlands)
Number of pages5
ISBN (Electronic)978-9-0827-9705-3
Publication statusPublished - 1 Aug 2020
EventEUSIPCO 2020: The 28th European Signal Processing Conference - Amsterdam, Netherlands
Duration: 18 Jan 202122 Jan 2021
Conference number: 28th

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


ConferenceEUSIPCO 2020
OtherDate change due to COVID-19 (former date August 24-28 2020)

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project
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.


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


Dive into the research topics of 'Privacy-Preserving Distributed Graph Filtering'. Together they form a unique fingerprint.

Cite this