Search Results for author:
Found 19 papers, 7 papers with code
Instance-hiding Schemes for Private Distributed Learning
The new ideas in the current paper are: (a) new variants of mixup with negative as well as positive coefficients, and extend the sample-wise mixup to be pixel-wise.
A Safe Harbor for AI Evaluation and Red Teaming
Independent evaluation and red teaming are critical for identifying the risks posed by generative AI systems.
Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications
We develop methods to identify critical regions that are vital for safety guardrails, and that are disentangled from utility-relevant regions at both the neuron and rank levels.
Detecting Pretraining Data from Large Language Models
Min-K% Prob can be applied without any knowledge about the pretraining corpus or any additional training, departing from previous detection methods that require training a reference model on data that is similar to the pretraining data.
Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation
Finally, we propose an effective alignment method that explores diverse generation strategies, which can reasonably reduce the misalignment rate under our attack.
Learning across Data Owners with Joint Differential Privacy
In this paper, we study the setting in which data owners train machine learning models collaboratively under a privacy notion called joint differential privacy [Kearns et al., 2018].
Privacy Implications of Retrieval-Based Language Models
Crucially, we find that $k$NN-LMs are more susceptible to leaking private information from their private datastore than parametric models.
Matching-based Data Valuation for Generative Model
To the best of their knowledge, GMValuator is the first work that offers a training-free, post-hoc data valuation strategy for deep generative models.
$k$NN-Adapter: Efficient Domain Adaptation for Black-Box Language Models
Fine-tuning a language model on a new domain is standard practice for domain adaptation.
Recovering Private Text in Federated Learning of Language Models
For the first time, we show the feasibility of recovering text from large batch sizes of up to 128 sentences.
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning
Gradient inversion attack (or input recovery from gradient) is an emerging threat to the security and privacy preservation of Federated learning, whereby malicious eavesdroppers or participants in the protocol can recover (partially) the clients' private data.
EMA: Auditing Data Removal from Trained Models
In this paper, we propose a new method called Ensembled Membership Auditing (EMA) for auditing data removal to overcome these limitations.
IFGAN: Missing Value Imputation using Feature-specific Generative Adversarial Networks
Missing value imputation is a challenging and well-researched topic in data mining.
MixCon: Adjusting the Separability of Data Representations for Harder Data Recovery
To address the issue that deep neural networks (DNNs) are vulnerable to model inversion attacks, we design an objective function, which adjusts the separability of the hidden data representations, as a way to control the trade-off between data utility and vulnerability to inversion attacks.
TextHide: Tackling Data Privacy in Language Understanding Tasks
In addition, TextHide fits well with the popular framework of fine-tuning pre-trained language models (e. g., BERT) for any sentence or sentence-pair task.
InstaHide: Instance-hiding Schemes for Private Distributed Learning
This paper introduces InstaHide, a simple encryption of training images, which can be plugged into existing distributed deep learning pipelines.
Deep Learning Based Detection and Localization of Intracranial Aneurysms in Computed Tomography Angiography
Purpose: To develop CADIA, a supervised deep learning model based on a region proposal network coupled with a false-positive reduction module for the detection and localization of intracranial aneurysms (IA) from computed tomography angiography (CTA), and to assess our model's performance to a similar detection network.
Privacy-preserving Learning via Deep Net Pruning
This paper attempts to answer the question whether neural network pruning can be used as a tool to achieve differential privacy without losing much data utility.
Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Net
Segmentation of pancreas is important for medical image analysis, yet it faces great challenges of class imbalance, background distractions and non-rigid geometrical features.
