6 citations · 6 across the 3 of their papers we have counts for
3 papers
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning
Taejin Kim, Jiarui Li, Shubhranshu Singh +2
The delicate equilibrium between user privacy and the ability to unleash the potential of distributed data is an important concern. Federated learning, which enables the training o…
Characterizing Internal Evasion Attacks in Federated Learning
Taejin Kim, Shubhranshu Singh, Nikhil Madaan +1
Federated learning allows for clients in a distributed system to jointly train a machine learning model. However, clients' models are vulnerable to attacks during the training and…
Can we Generalize and Distribute Private Representation Learning?
Sheikh Shams Azam, Taejin Kim, Seyyedali Hosseinalipour +3
We study the problem of learning representations that are private yet informative, i.e., provide information about intended "ally" targets while hiding sensitive "adversary" attrib…