10 citations · 16 across the 4 of their papers we have counts for
4 papers
Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation
Qiongxiu Li, Jaron Skovsted Gundersen, Katrine Tjell +2
Privacy has become a major concern in machine learning. In fact, the federated learning is motivated by the privacy concern as it does not allow to transmit the private data but on…
Secure learning-based MPC via garbled circuit
K. Tjell, N. Schlüter, P. Binfet +1
Encrypted control seeks confidential controller evaluation in cloud-based or networked systems. Many existing approaches build on homomorphic encryption (HE) that allow simple math…
Privacy in Distributed Computations based on Real Number Secret Sharing
Katrine Tjell, Rafael Wisniewski
Privacy preservation in distributed computations is an important subject as digitization and new technologies enable collection and storage of vast amounts of data, including priva…
Privacy Preservation in Epidemic Data Collection
Katrine Tjell, Jaron Skovsted Gundersen, Rafael Wisniewski
This work is inspired by the outbreak of COVID-19, and some of the challenges we have observed with gathering data about the disease. To this end, we aim to help collect data about…