TY - GEN
T1 - Finding a Choice in a Haystack
T2 - 29th International World Wide Web Conference, WWW 2020
AU - Bannihatti Kumar, Vinayshekhar
AU - Iyengar, Roger
AU - Nisal, Namita
AU - Feng, Yuanyuan
AU - Habib, Hana
AU - Story, Peter
AU - Cherivirala, Sushain
AU - Hagan, Margaret
AU - Cranor, Lorrie
AU - Wilson, Shomir
AU - Schaub, Florian
AU - Sadeh, Norman
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/4/20
Y1 - 2020/4/20
N2 - Website privacy policies sometimes provide users the option to opt-out of certain collections and uses of their personal data. Unfortunately, many privacy policies bury these instructions deep in their text, and few web users have the time or skill necessary to discover them. We describe a method for the automated detection of opt-out choices in privacy policy text and their presentation to users through a web browser extension. We describe the creation of two corpora of opt-out choices, which enable the training of classifiers to identify opt-outs in privacy policies. Our overall approach for extracting and classifying opt-out choices combines heuristics to identify commonly found opt-out hyperlinks with supervised machine learning to automatically identify less conspicuous instances. Our approach achieves a precision of 0.93 and a recall of 0.9. We introduce Opt-Out Easy, a web browser extension designed to present available opt-out choices to users as they browse the web. We evaluate the usability of our browser extension with a user study. We also present results of a large-scale analysis of opt-outs found in the text of thousands of the most popular websites.
AB - Website privacy policies sometimes provide users the option to opt-out of certain collections and uses of their personal data. Unfortunately, many privacy policies bury these instructions deep in their text, and few web users have the time or skill necessary to discover them. We describe a method for the automated detection of opt-out choices in privacy policy text and their presentation to users through a web browser extension. We describe the creation of two corpora of opt-out choices, which enable the training of classifiers to identify opt-outs in privacy policies. Our overall approach for extracting and classifying opt-out choices combines heuristics to identify commonly found opt-out hyperlinks with supervised machine learning to automatically identify less conspicuous instances. Our approach achieves a precision of 0.93 and a recall of 0.9. We introduce Opt-Out Easy, a web browser extension designed to present available opt-out choices to users as they browse the web. We evaluate the usability of our browser extension with a user study. We also present results of a large-scale analysis of opt-outs found in the text of thousands of the most popular websites.
UR - http://www.scopus.com/inward/record.url?scp=85086597865&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85086597865&partnerID=8YFLogxK
U2 - 10.1145/3366423.3380262
DO - 10.1145/3366423.3380262
M3 - Conference contribution
AN - SCOPUS:85086597865
T3 - The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
SP - 1943
EP - 1954
BT - The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
PB - Association for Computing Machinery, Inc
Y2 - 20 April 2020 through 24 April 2020
ER -