Abstract
Dynamic graphs are essential for analyzing complex systems like social or communication networks, allowing researchers to study behaviors and evolution over time. Edge privacy, a key concern in dynamic graphs, involves safeguarding sensitive information about individual connections while the network structure evolves. This paper explores the feasibility of protecting dynamic graphs with differential privacy and using them for effective link prediction, emphasizing the importance of integrating privacy measures into dynamic graph analysis. We evaluate the performance of link prediction algorithms on protected graphs, demonstrating how privacy-enhancing techniques can bolster the robustness and confidentiality of link prediction within evolving network environments. Our study contributes towards establishing more secure and dependable analyses of dynamic network structures by showcasing the practical benefits of edge privacy in link prediction tasks.
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Acknowledgements
This work is linked to the project SECURING PID2021-125962OB-C31, funded by the Spanish Ministry of Science and Innovation, la Agencia Estatal de Investigación and the European Regional Development Fund (FEDER fund). It is also supported by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union - NextGenerationEU, in the framework of the Recovery Plan, Transformation and Resilience, under the Calls UNICO I+D 5G 2021 (ref. number TSI-063000-2021-13- 6GENABLERS-SEC), and INCIBE (ARTEMISA International Chair of Cybersecurity and DANGER Strategic Project of Cybersecurity).
This work was partially supported by the Spanish Ministry under Grant PID2021-125962OB-C33 SECURING/NET, by the Catalan Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) under Grant SGR2021-00643 and by the Plan de Recuperación, Transformación y Resiliencia funded with Next Generation EU funds through the project DANGER INCIBE-C062/23.
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Salas, J., Borrego, C. (2024). Studying the Impact of Edge Privacy on Link Prediction in Temporal Graphs. In: Torra, V., Narukawa, Y., Kikuchi, H. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2024. Lecture Notes in Computer Science(), vol 14986. Springer, Cham. https://doi.org/10.1007/978-3-031-68208-7_15
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DOI: https://doi.org/10.1007/978-3-031-68208-7_15
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