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Add CITATION.cff file with metadata for software citation
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danielgoldelman committed Apr 12, 2024
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cff-version: 1.2.0
title: Privacy Pioneer
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Sebastian
family-names: Zimmeck
affiliation: Wesleyan University
- given-names: Daniel
family-names: Goldelman
affiliation: Wesleyan University
- given-names: Owen
family-names: Kaplan
affiliation: Wesleyan University
- given-names: Logan
family-names: Brown
affiliation: Wesleyan University
- given-names: Justin
family-names: Casler
affiliation: Wesleyan University
- given-names: Judely
family-names: Jean-Charles
affiliation: Wesleyan University
- given-names: Joe
family-names: Champeau
affiliation: Wesleyan University
- given-names: Hamza
family-names: Harkous
affiliation: Google
identifiers:
- type: url
value: >-
https://petsymposium.org/popets/2024/popets-2024-0048.php
description: PETS Publication
repository-code: 'https://github.com/privacy-tech-lab/privacy-pioneer'
abstract: >-
Data collection by websites and their integrated third
parties is often not transparent. We design privacy
interfaces for the browser to help people understand who
is collecting which data from them. In a proof of concept
browser extension, Privacy Pioneer , we implement a
privacy popup, a privacy history interface, and a
watchlist to notify people when their data is collected.
For detecting location data collection, we develop a
machine learning model based on TinyBERT, which reaches an
average F1 score of 0.94. We supplement our model with
deterministic methods to detect trackers, collection of
personal data, and other monetization techniques. In a
usability study with 100 participants 82% found Privacy
Pioneer easy to understand and 90% found it useful
indicating the value of privacy interfaces directly
integrated in the browser
keywords:
- Web Privacy
- Data Transparency
- Privacy Dashboards
- Notice and Choice
- Consent
- Privacy Labels
- Usability
- Privacy Enhancing Technologies
- Web Traffic Analysis
- BERT
- Machine Learning
license: CC-BY-NC-SA-4.0
commit: 7c1ab1c
version: v1.2.2
date-released: '2023-11-19'

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