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.
|Title of host publication||28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings|
|Place of Publication||Amsterdam (Netherlands)|
|Number of pages||5|
|Publication status||Published - 1 Aug 2020|
|Event||EUSIPCO 2020: The 28th European Signal Processing Conference - Amsterdam, Netherlands|
Duration: 18 Jan 2021 → 22 Jan 2021
Conference number: 28th
|Name||European Signal Processing Conference|
|Period||18/01/21 → 22/01/21|
|Other||Date change due to COVID-19 (former date August 24-28 2020)|
Bibliographical noteGreen 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.
- Distributed computation
- Distributed graph filters
- Graph signal processing