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09/11/2023
Privacy Side Channels in Machine Learning Systems
Most current approaches for protecting privacy in machine learning (ML) ...
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09/09/2023
Reverse-Engineering Decoding Strategies Given Blackbox Access to a Language Generation System
Neural language models are increasingly deployed into APIs and websites ...
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06/26/2023
Are aligned neural networks adversarially aligned?
Large language models are now tuned to align with the goals of their cre...
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05/15/2023
Privacy Auditing with One (1) Training Run
We propose a scheme for auditing differentially private machine learning...
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05/10/2023
Privacy-Preserving Recommender Systems with Synthetic Query Generation using Differentially Private Large Language Models
We propose a novel approach for developing privacy-preserving large-scal...
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03/06/2023
Students Parrot Their Teachers: Membership Inference on Model Distillation
Model distillation is frequently proposed as a technique to reduce the p...
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02/19/2023
Why Is Public Pretraining Necessary for Private Model Training?
In the privacy-utility tradeoff of a model trained on benchmark language...
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02/15/2023
Tight Auditing of Differentially Private Machine Learning
Auditing mechanisms for differential privacy use probabilistic means to ...
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01/30/2023
Extracting Training Data from Diffusion Models
Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion ha...
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10/31/2022
Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy
Studying data memorization in neural language models helps us understand...
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09/29/2022
No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy"
New methods designed to preserve data privacy require careful scrutiny. ...
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06/07/2022
FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning
Federated learning (FL) has emerged as an effective approach to address ...
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10/15/2021
Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture
Membership inference attacks are a key measure to evaluate privacy leaka...
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02/01/2021
Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems
Deep Neural Networks (DNNs) have become prevalent in wireless communicat...
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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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02/16/2020
Blind Adversarial Network Perturbations
Deep Neural Networks (DNNs) are commonly used for various traffic analys...
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09/05/2019
Bidding Strategies with Gender Nondiscrimination: Constraints for Online Ad Auctions
Interactions between bids to show ads online can lead to an advertiser's...
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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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08/22/2018
DeepCorr: Strong Flow Correlation Attacks on Tor Using Deep Learning
Flow correlation is the core technique used in a multitude of deanonymiz...
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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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09/12/2017