3 papers
cs.IT2026
Differentially Private Spectral Graph Clustering: Balancing Privacy, Accuracy, and Efficiency
Antti Koskela, Mohamed Seif, H. Vincent Poor +1
We study spectral graph clustering under edge differential privacy. We propose a matrix shuffling mechanism that combines randomized edge flipping with a random permutation of the…
cs.SI2025
On the Price of Differential Privacy for Spectral Clustering over Stochastic Block Models
Antti Koskela, Mohamed Seif, Andrea J. Goldsmith
We investigate privacy-preserving spectral clustering for community detection within stochastic block models (SBMs). Specifically, we focus on edge differential privacy (DP) and pr…
cs.DC2025
Protecting Confidentiality, Privacy and Integrity in Collaborative Learning
Dong Chen, Alice Dethise, Istemi Ekin Akkus +6
A collaboration between dataset owners and model owners is needed to facilitate effective machine learning (ML) training. During this collaboration, however, dataset owners and mod…