91 citations · 341 across the 12 of their papers we have counts for
20 papers
Data Privacy and Trustworthy Machine Learning
Martin Strobel, Reza Shokri
The privacy risks of machine learning models is a major concern when training them on sensitive and personal data. We discuss the tradeoffs between data privacy and the remaining g…
What Does it Mean for a Language Model to Preserve Privacy?
Hannah Brown, Katherine Lee, Fatemehsadat Mireshghallah +2
Natural language reflects our private lives and identities, making its privacy concerns as broad as those of real life. Language models lack the ability to understand the context a…
On the Privacy Risks of Algorithmic Fairness
Hongyan Chang, Reza Shokri
Algorithmic fairness and privacy are essential pillars of trustworthy machine learning. Fair machine learning aims at minimizing discrimination against protected groups by, for exa…
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…
Improving Deep Learning with Differential Privacy using Gradient Encoding and Denoising
Milad Nasr, Reza Shokri, Amir houmansadr
Deep learning models leak significant amounts of information about their training datasets. Previous work has investigated training models with differential privacy (DP) guarantees…
ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning
Sasi Kumar Murakonda, Reza Shokri
When building machine learning models using sensitive data, organizations should ensure that the data processed in such systems is adequately protected. For projects involving mach…