Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning
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In Alan Westin's generally accepted definition of privacy, he describes it as an individual's right 'to control, edit, manage, and delete information about them[selves] and decide when, how, and to what extent information is communicated to others.' There ...
The ever-growing number of edge devices (e.g., smartphones) and the exploding volume of sensitive data they produce, call for distributed machine learning techniques that are privacy-preserving. Given the increasing computing capabilities of modern edge de ...
To help fighting the COVID-19 pandemic, the Pan-European Privacy-Preserving Proximity Tracing (PEPP-PT) project proposed a Decentralized Privacy-Preserving Proximity Tracing (DP3T) system. This helps tracking the spread of SARS-CoV-2 virus while keeping th ...
In this thesis, we focus on the problem of achieving practical privacy guarantees in machine learning (ML), where the classic differential privacy (DP) fails to maintain a good trade-off between user privacy and data utility. Differential privacy guarantee ...
In human-computer interaction, self-disclosure of sensitive information regarding distressing experiences requires the establishment of a trust channel between the user and the digital tool. As privacy and security have been identified as factors that cont ...
The COVID-19 pandemic created a noticeable challenge to the cryptographic community with the development of contact tracing applications. The media reported a dispute between designers proposing a centralized or a decentralized solution (namely, the PEPP-P ...
%0 Conference Paper %T Bayesian Differential Privacy for Machine Learning %A Aleksei Triastcyn %A Boi Faltings %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E A ...
This paper considers the problem of enhancing user privacy in common machine learning development tasks, such as data annotation and inspection, by substituting the real data with samples form a generative adversarial network. We propose employing Bayesian ...
