90 citations · 331 across the 28 of their papers we have counts for
6 papers · 1 filter
MACE: A Flexible Framework for Membership Privacy Estimation in Generative Models
Yixi Xu, Sumit Mukherjee, Xiyang Liu +3
Generative machine learning models are being increasingly viewed as a way to share sensitive data between institutions. While there has been work on developing differentially priva…
SOTERIA: In Search of Efficient Neural Networks for Private Inference
Anshul Aggarwal, Trevor E. Carlson, Reza Shokri +1
ML-as-a-service is gaining popularity where a cloud server hosts a trained model and offers prediction (inference) service to users. In this setting, our objective is to protect th…
Domain Generalization using Causal Matching
Divyat Mahajan, Shruti Tople, Amit Sharma
In the domain generalization literature, a common objective is to learn representations independent of the domain after conditioning on the class label. We show that this objective…
Leakage of Dataset Properties in Multi-Party Machine Learning
Wanrong Zhang, Shruti Tople, Olga Ohrimenko
Secure multi-party machine learning allows several parties to build a model on their pooled data to increase utility while not explicitly sharing data with each other. We show that…
FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning
Sameer Wagh, Shruti Tople, Fabrice Benhamouda +3
We propose Falcon, an end-to-end 3-party protocol for efficient private training and inference of large machine learning models. Falcon presents four main advantages - (i) It is hi…
To Transfer or Not to Transfer: Misclassification Attacks Against Transfer Learned Text Classifiers
Bijeeta Pal, Shruti Tople
Transfer learning --- transferring learned knowledge --- has brought a paradigm shift in the way models are trained. The lucrative benefits of improved accuracy and reduced trainin…