Privacy and Transparency in Graph Machine Learning: A Unified Perspective

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Abstract

Graph Machine Learning (GraphML), whereby classical machine learning is generalized to irregular graph domains, has enjoyed a recent renaissance, leading to a dizzying array of models and their applications in several domains. With its growing applicability to sensitive domains and regulations by governmental agencies for trustworthy AI systems, researchers have started looking into the issues of transparency and privacy of graph learning. However, these topics have been mainly investigated independently. In this position paper, we provide a unified perspective on the interplay of privacy and transparency in GraphML. In particular, we describe the challenges and possible research directions for a formal investigation of privacy-transparency tradeoffs in GraphML.
Original languageEnglish
Title of host publicationAIMLAI’22: In Proceedings of Advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI) at CIKM’22
EditorsGeorgios Drakopoulos , Eleanna Kafeza
Number of pages6
Publication statusPublished - 2022
EventCIKM 2022 31st ACM International Conference on Information and Knowledge Management (CIKM 2022) - Atlanta, United States
Duration: 17 Oct 202221 Oct 2022

Publication series

NameCeur Workshop Proceedings
Volume3318
ISSN (Electronic)1613-0073

Conference

ConferenceCIKM 2022 31st ACM International Conference on Information and Knowledge Management (CIKM 2022)
Abbreviated titleCIKM 2022
Country/TerritoryUnited States
CityAtlanta
Period17/10/2221/10/22

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