1.6k citations · 2.6k across the 33 of their papers we have counts for
11 papers · 1 filter
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets
Florian Tramèr, Reza Shokri, Ayrton San Joaquin +4
We introduce a new class of attacks on machine learning models. We show that an adversary who can poison a training dataset can cause models trained on this dataset to leak signifi…
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…
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…
Epione: Lightweight Contact Tracing with Strong Privacy
Ni Trieu, Kareem Shehata, Prateek Saxena +2
Contact tracing is an essential tool in containing infectious diseases such as COVID-19. Many countries and research groups have launched or announced mobile apps to facilitate con…
Chiron: Privacy-preserving Machine Learning as a Service
Tyler Hunt, Congzheng Song, Reza Shokri +2
Major cloud operators offer machine learning (ML) as a service, enabling customers who have the data but not ML expertise or infrastructure to train predictive models on this data.…
Plausible Deniability for Privacy-Preserving Data Synthesis
Vincent Bindschaedler, Reza Shokri, Carl A. Gunter
Releasing full data records is one of the most challenging problems in data privacy. On the one hand, many of the popular techniques such as data de-identification are problematic…
Membership Inference Attacks against Machine Learning Models
Reza Shokri, Marco Stronati, Congzheng Song +1
We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference…
Defeating Image Obfuscation with Deep Learning
Richard McPherson, Reza Shokri, Vitaly Shmatikov
We demonstrate that modern image recognition methods based on artificial neural networks can recover hidden information from images protected by various forms of obfuscation. The o…
Privacy through Fake yet Semantically Real Traces
Vincent Bindschaedler, Reza Shokri
Camouflaging data by generating fake information is a well-known obfuscation technique for protecting data privacy. In this paper, we focus on a very sensitive and increasingly exp…
Prolonging the Hide-and-Seek Game: Optimal Trajectory Privacy for Location-Based Services
George Theodorakopoulos, Reza Shokri, Carmela Troncoso +2
Human mobility is highly predictable. Individuals tend to only visit a few locations with high frequency, and to move among them in a certain sequence reflecting their habits and d…
Privacy Games: Optimal User-Centric Data Obfuscation
Reza Shokri
In this paper, we design user-centric obfuscation mechanisms that impose the minimum utility loss for guaranteeing user's privacy. We optimize utility subject to a joint guarantee…