Scalable Learning with Privacy over Graphs

Yanning Shen, Geert Leus

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

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Graphs have well-documented merits for modeling complex systems, including financial, biological, and social networks. Network nodes can also include attributes such as age or gender of users in a social network. However, the size of real-world networks can be massive, and nodal attributes can be unavailable. Moreover, new nodes may emerge over time, and their attributes must be inferred in real time. In this context, the present paper deals with scalable learning of nodal attributes by estimating a nodal function based on noisy observations at a subset of nodes. A multikernel-based approach is developed which is scalable to large-size networks. The novel method is capable of providing real-time evaluation of the function values on newly-joining nodes without resorting to a batch solver. In addition, the novel scheme only relies on an encrypted version of each node's connectivity, which promotes privacy. Experiments on real datasets corroborate the effectiveness of the proposed methods.

Original languageEnglish
Title of host publication2019 IEEE Data Science Workshop, DSW 2019 - Proceedings
Subtitle of host publicationProceedings
Place of PublicationPiscataway
Number of pages5
ISBN (Electronic)978-1-7281-0708-0
ISBN (Print)978-1-7281-0709-7
Publication statusPublished - 2019
Event2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States
Duration: 2 Jun 20195 Jun 2019

Publication series

Name2019 IEEE Data Science Workshop, DSW 2019 - Proceedings


Conference2019 IEEE Data Science Workshop, DSW 2019
Country/TerritoryUnited States

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.


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