Aubin Birba

Montréal, Québec, Canada

I’m a cybersecurity researcher and engineer with deep expertise in cryptography, privacy-preserving technologies, and machine learning systems. My work spans both academic research and industry applications, focusing on designing and implementing secure protocols, privacy-enhancing technologies, and robust machine learning infrastructures.

I am passionate about bridging the gap between theoretical advances and practical deployments, ensuring that innovative security solutions are accessible and effective in real-world environments. I have contributed to projects ranging from tailored secure software development methodologies to secure computation libraries and privacy-preserving analytics, with applications in various sectors such as industry, transport, and healthcare.

Selected publications

2025

GRAND : Graph Reconstruction from Potential Partial Adjacency and Neighborhood Data

Sofiane Azogagh, Zelma Aubin Birba, Josée Desharnais, and 3 more authors

In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2, 2025

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Cryptographic approaches, such as secure multiparty computation, can be used to securely compute a function of a distributed graph without centralizing the data of each participant. However, the output of the protocol can leak sensitive information about the structure of the original graph. In particular, we propose an approach by which an adversary observing the result of a private protocol for the computation of the number of common neighbors between all pairs of vertices, can reconstruct the adjacency matrix of the graph. In fact, this can only be done up to co-squareness, a notion we introduce, as two different graphs can have the same matrix of common neighbors. To realize this, we consider two adversary models, one who observes the common neighbors matrix only and a more informed one that has partial knowledge of the original graph. Our results demonstrate that, from their common neighbors matrix, graphs can be reconstructed with high accuracy (up to co-squareness). The proposed reconstruction is also interesting in itself from the point of view of graph theory.

2024

Crypto’Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph Learning

Sofiane Azogagh, Zelma Aubin Birba, Sébastien Gambs, and 1 more author

In Proceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy, 2024

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Graphs are a widely used data structure for collecting and analyzing relational data. However, when the graph structure is distributed across several parties, its analysis is challenging. In particular, due to the sensitivity of the data each party might want to keep their partial knowledge of the graph private, while still be willing to collaborate with the other parties for tasks of mutual benefit, such as data curation or the removal of poisoned data. To address this challenge, we propose Crypto’Graph, an efficient protocol for privacy-preserving link prediction on distributed graphs. More precisely, it allows parties partially sharing a graph with distributed links to infer the likelihood of formation of new links in the future. Through the use of cryptographic primitives, Crypto’Graph is able to compute the likelihood of these new links on the joint network without revealing the structure of the private graph of each party, even though they know the number of nodes they have, since they share the same graph in terms of nodes but not the same links. Crypto’Graph improves on previous works by enabling the computation of a diverse set of similarity metrics in parallel without any additional cost. The use of Crypto’Graph is illustrated for defense against graph poisoning attacks, in which potential adversarial links are identified without compromising the privacy of the graphs of individual parties. The effectiveness of Crypto’Graph in mitigating graph poisoning attacks and achieving high prediction accuracy on a node classification task using graph neural networks is demonstrated through extensive experimentation on two real-world datasets.