Web tracking by ad networks and other data-driven businesses is often privacy-invasive. Privacy laws, such as the California Consumer Privacy Act, aim to give people more control over their data. In particular, they provide a right to opt out from web tracking via privacy preference signals, notably Global Privacy Control (GPC). GPC holds the promise of enabling people to exercise their opt out rights on the web. Broad adoption of GPC hinges on its usability. In a usability survey we find that 94% of the participants would turn on GPC indicating a need for such efficient and effective opt out mechanism. 81% of the participants in our survey also have a correct understanding of what GPC does ensuring that their intent is accurately represented by their choice. The effectiveness of GPC is dependent on whether websites' GPC compliance can be enforced. A site's GPC compliance can be analyzed based on privacy flags, such as the US Privacy String, which is used on many sites to indicate the opt out status of a web user. Leveraging the US Privacy String for GPC purposes we implement a proof-of-concept browser extension that successfully and correctly analyzes sites' GPC compliance at a rate of 89%. We further implement a web crawler for our browser extension demonstrating that our analysis approach is scalable. We find that many sites do not respect GPC opt out signals despite being legally obligated to do so. Only 54/464 (12%) sites with a US Privacy String opt out users after having received a GPC signal.
Finding a Choice in a Haystack: Automatic Extraction of Opt-Out Statements from Privacy Policy Text
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.
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- Award ID(s):
- 1914486
- NSF-PAR ID:
- 10169862
- Date Published:
- Journal Name:
- WWW '20: Proceedings of the Web Conference 2020
- Page Range / eLocation ID:
- 1943 to 1954
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3366423.3380262
