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09/05/2023
Bias Propagation in Federated Learning
We show that participating in federated learning can be detrimental to g...
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07/09/2023
On The Impact of Machine Learning Randomness on Group Fairness
Statistical measures for group fairness in machine learning reflect the ...
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05/17/2023
Smaller Language Models are Better Black-box Machine-Generated Text Detectors
With the advent of fluent generative language models that can produce co...
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10/17/2022
Forget Unlearning: Towards True Data-Deletion in Machine Learning
Unlearning has emerged as a technique to efficiently erase information o...
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09/14/2022
Data Privacy and Trustworthy Machine Learning
The privacy risks of machine learning models is a major concern when tra...
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03/31/2022
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets
We introduce a new class of attacks on machine learning models. We show ...
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03/10/2022
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence)
Differential privacy analysis of randomized learning algorithms typicall...
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03/08/2022
Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks
The wide adoption and application of Masked language models (MLMs) on se...
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02/11/2022
What Does it Mean for a Language Model to Preserve Privacy?
Natural language reflects our private lives and identities, making its p...
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11/18/2021
Enhanced Membership Inference Attacks against Machine Learning Models
How much does a given trained model leak about each individual data reco...
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02/11/2021
Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent
We model the dynamics of privacy loss in Langevin diffusion and extend i...
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11/07/2020
On the Privacy Risks of Algorithmic Fairness
Algorithmic fairness and privacy are essential elements of trustworthy m...
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07/25/2020
SOTERIA: In Search of Efficient Neural Networks for Private Inference
ML-as-a-service is gaining popularity where a cloud server hosts a train...
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07/22/2020
Improving Deep Learning with Differential Privacy using Gradient Encoding and Denoising
Deep learning models leak significant amounts of information about their...
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07/18/2020
ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning
When building machine learning models using sensitive data, organization...
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06/16/2020
Model Explanations with Differential Privacy
Black-box machine learning models are used in critical decision-making d...
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06/15/2020
On Adversarial Bias and the Robustness of Fair Machine Learning
Optimizing prediction accuracy can come at the expense of fairness. Towa...
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04/28/2020
Epione: Lightweight Contact Tracing with Strong Privacy
Contact tracing is an essential tool in containing infectious diseases s...
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12/24/2019
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer
Collaborative (federated) learning enables multiple parties to train a m...
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09/27/2019
Membership Encoding for Deep Learning
Machine learning as a service (MLaaS), and algorithm marketplaces are on...
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06/29/2019
Privacy Risks of Explaining Machine Learning Models
Can we trust black-box machine learning with its decisions? Can we trust...
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05/31/2019
Bypassing Backdoor Detection Algorithms in Deep Learning
Deep learning models are known to be vulnerable to various adversarial m...
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05/29/2019
Ultimate Power of Inference Attacks: Privacy Risks of High-Dimensional Models
Models leak information about their training data. This enables attacker...
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05/24/2019
Privacy Risks of Securing Machine Learning Models against Adversarial Examples
The arms race between attacks and defenses for machine learning models h...
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12/03/2018
Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks
Deep neural networks are susceptible to various inference attacks as the...
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07/16/2018
Machine Learning with Membership Privacy using Adversarial Regularization
Machine learning models leak information about the datasets on which the...
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03/15/2018
Chiron: Privacy-preserving Machine Learning as a Service
Major cloud operators offer machine learning (ML) as a service, enabling...
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08/26/2017
Plausible Deniability for Privacy-Preserving Data Synthesis
Releasing full data records is one of the most challenging problems in d...
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10/18/2016
Membership Inference Attacks against Machine Learning Models
We quantitatively investigate how machine learning models leak informati...
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09/01/2016
