Search Results for author: David Lie

Found 8 papers, 3 papers with code

Machine Unlearning

2 code implementations9 Dec 2019 Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, Nicolas Papernot

Once users have shared their data online, it is generally difficult for them to revoke access and ask for the data to be deleted.

Machine Unlearning Transfer Learning

In Differential Privacy, There is Truth: On Vote Leakage in Ensemble Private Learning

1 code implementation22 Sep 2022 Jiaqi Wang, Roei Schuster, Ilia Shumailov, David Lie, Nicolas Papernot

When learning from sensitive data, care must be taken to ensure that training algorithms address privacy concerns.

Ocasta: Clustering Configuration Settings For Error Recovery

no code implementations2 Nov 2017 Zhen Huang, David Lie

However, a recent study found that a significant amount of configuration errors require fixing more than one setting together.

Clustering

BinPro: A Tool for Binary Source Code Provenance

no code implementations2 Nov 2017 Dhaval Miyani, Zhen Huang, David Lie

Enforcing open source licenses such as the GNU General Public License (GPL), analyzing a binary for possible vulnerabilities, and code maintenance are all situations where it is useful to be able to determine the source code provenance of a binary.

Cryptography and Security D.4.6

Deep Active Learning with Crowdsourcing Data for Privacy Policy Classification

no code implementations7 Aug 2020 Wenjun Qiu, David Lie

While automated tools based on machine learning exist for privacy policy analysis, to achieve high classification accuracy, classifiers need to be trained on a large labeled dataset.

Active Learning Classification +1

Implementing Active Learning in Cybersecurity: Detecting Anomalies in Redacted Emails

no code implementations1 Mar 2023 Mu-Huan Chung, Lu Wang, Sharon Li, Yuhong Yang, Calvin Giang, Khilan Jerath, Abhay Raman, David Lie, Mark Chignell

In this paper we present research results concerning the application of Active Learning to anomaly detection in redacted emails, comparing the utility of different methods for implementing active learning in this context.

Active Learning Anomaly Detection

Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active Learning

1 code implementation16 Jan 2024 Wenjun Qiu, David Lie, Lisa Austin

To address these challenges, we present Calpric , which combines automatic text selection and segmentation, active learning and the use of crowdsourced annotators to generate a large, balanced training set for privacy policies at low cost.

Active Learning

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