Scalable Learning with Privacy over Graphs

Scalable Learning with Privacy over Graphs

Shen, Yanning (University of Minnesota Twin Cities)
Leus, G.J.T. (TU Delft Signal Processing Systems)

2019

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.

http://resolver.tudelft.nl/uuid:61acc2e0-a766-4ccf-835f-20aa3cba15f7

https://doi.org/10.1109/DSW.2019.8755782

IEEE, Piscataway

2020-01-04

978-1-7281-0709-7

2019 IEEE Data Science Workshop, DSW 2019 - Proceedings: Proceedings

2019 IEEE Data Science Workshop, DSW 2019, 2019-06-02 → 2019-06-05, Minneapolis, United States

2019 IEEE Data Science Workshop, DSW 2019 - Proceedings

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

© 2019 Yanning Shen, G.J.T. Leus