DiVa: An Accelerator for Differentially Private Machine Learning. (arXiv:2208.12392v1 [cs.AR])

The widespread deployment of machine learning (ML) is raising serious
concerns on protecting the privacy of users who contributed to the collection
of training data. Differential privacy (DP) is rapidly gaining momentum in the
industry as a practical standard for privacy protection. Despite DP's
importance, however, little has been explored within the computer systems
community regarding the implication of this emerging ML algorithm on system
designs. In this work, we conduct a detailed workload characterization on a
state-of-the-art differentially private …

ar machine machine learning