Milad Nasr | DeepAI

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∙ 09/11/2023

Privacy Side Channels in Machine Learning Systems

Most current approaches for protecting privacy in machine learning (ML) ...

0 Edoardo Debenedetti, et al. ∙

∙ 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 ...

0 Daphne Ippolito, et al. ∙

∙ 06/26/2023

Are aligned neural networks adversarially aligned?

Large language models are now tuned to align with the goals of their cre...

0 Nicholas Carlini, et al. ∙

∙ 05/15/2023

Privacy Auditing with One (1) Training Run

We propose a scheme for auditing differentially private machine learning...

0 Thomas Steinke, et al. ∙

∙ 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...

0 Aldo Gael Carranza, et al. ∙

∙ 03/06/2023

Students Parrot Their Teachers: Membership Inference on Model Distillation

Model distillation is frequently proposed as a technique to reduce the p...

0 Matthew Jagielski, et al. ∙

∙ 02/19/2023

Why Is Public Pretraining Necessary for Private Model Training?

In the privacy-utility tradeoff of a model trained on benchmark language...

0 Arun Ganesh, et al. ∙

∙ 02/15/2023

Tight Auditing of Differentially Private Machine Learning

Auditing mechanisms for differential privacy use probabilistic means to ...

0 Milad Nasr, et al. ∙

∙ 01/30/2023

Extracting Training Data from Diffusion Models

Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion ha...

0 Nicholas Carlini, et al. ∙

∙ 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...

0 Daphne Ippolito, et al. ∙

∙ 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. ...

0 Nicholas Carlini, et al. ∙

∙ 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 ...

0 Meisam Hejazinia, et al. ∙

∙ 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...

5 Xinyu Tang, et al. ∙

∙ 02/01/2021

Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems

Deep Neural Networks (DNNs) have become prevalent in wireless communicat...

0 Alireza Bahramali, et al. ∙

∙ 07/22/2020

Improving Deep Learning with Differential Privacy using Gradient Encoding and Denoising

Deep learning models leak significant amounts of information about their...

0 Milad Nasr, et al. ∙

∙ 02/16/2020

Blind Adversarial Network Perturbations

Deep Neural Networks (DNNs) are commonly used for various traffic analys...

0 Milad Nasr, et al. ∙

∙ 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...

0 Milad Nasr, et al. ∙

∙ 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...

0 Milad Nasr, et al. ∙

∙ 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...

0 Milad Nasr, et al. ∙

∙ 07/16/2018

Machine Learning with Membership Privacy using Adversarial Regularization

Machine learning models leak information about the datasets on which the...

0 Milad Nasr, et al. ∙

∙ 09/12/2017

Enemy At the Gateways: A Game Theoretic Approach to Proxy Distribution

A core technique used by popular proxy-based circumvention systems like ...

0 Milad Nasr, et al. ∙

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