Search Results for author:
Found 4 papers, 2 papers with code
FastLloyd: Federated, Accurate, Secure, and Tunable $k$-Means Clustering with Differential Privacy
By utilizing the computational DP model, we design a lightweight, secure aggregation-based approach that achieves four orders of magnitude speed-up over state-of-the-art related work.
Fast and Private Inference of Deep Neural Networks by Co-designing Activation Functions
Motivated by the success of previous work co-designing machine learning and MPC, we develop an activation function co-design.
Self-Attention Generative Adversarial Network for Iterative Reconstruction of CT Images
Conventional reconstruction algorithms can be used if the X-ray data are adequately sampled and of high quality; however, concerns such as reducing dose to the patient, or geometric limitations on data acquisition, may result in low quality or incomplete data.
Investigating Membership Inference Attacks under Data Dependencies
Our results reveal that training set dependencies can severely increase the performance of MIAs, and therefore assuming that data samples are statistically independent can significantly underestimate the performance of MIAs.
