Selected Publications

In this paper, we present the first differentially private clustering method for arbitrary-shaped node clusters in a graph. This algorithm takes as input only an approximate Minimum Spanning Tree (MST) T released under weight differential privacy constraints from the graph. Then, the underlying nonconvex clustering partition is successfully recovered from cutting optimal cuts on T. As opposed to existing methods, our algorithm is theoretically well-motivated. Experiments support our theoretical findings.
UAI, 2018

Recent Publications

. Graph-based Clustering under Differential Privacy. UAI, 2018.

PDF Arxiv

. Minimum spanning tree release under differential privacy constraints. Master Thesis in Statistics, Université Paris 6 Pierre et Marie Curie, 2017.

PDF Arxiv

Recent Posts

Awarded with JSPS Summer program scholarship!


The second edition of the Paris-Saclay Junior Conference on Data Science and Engineering is addressed to PhD students in their first year, M2 students and third year students at Engineering schools at Paris-Saclay. It offers these students the opportunity to present their scientific works developed at internships, or in the first year of thesis, and also to grow their critical sense thanks to a professional conference hosting prestigious invited speakers, academics and industry scientists.





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