2 code implementations • 9 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.
1 code implementation • 22 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.
no code implementations • 2 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.
no code implementations • 2 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
no code implementations • 7 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.
no code implementations • 3 Aug 2021 • Adelin Travers, Lorna Licollari, Guanghan Wang, Varun Chandrasekaran, Adam Dziedzic, David Lie, Nicolas Papernot
In the white-box setting, we instantiate this class with a joint, multi-stage optimization attack.
no code implementations • 1 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.
1 code implementation • 16 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.