Crowdsourcing Annotations For Websites' Privacy Policies: Can It Really Work?

Keywords

Crowdsourcing; HCI.; Machine Learning; Privacy; Privacy Policies

Abstract

Website privacy policies are often long and difficult to understand. While research shows that Internet users care about their privacy, they do not have time to understand the policies of every website they visit, and most users hardly ever read privacy policies. Several recent efforts aim to crowdsource the interpretation of privacy policies and use the resulting annotations to build more effective user interfaces that provide users with salient policy summaries. However, very little attention has been devoted to studying the accuracy and scalability of crowdsourced privacy policy annotations, the types of questions crowdworkers can effectively answer, and the ways in which their productivity can be enhanced. Prior research indicates that most Internet users often have great difficulty understanding privacy policies, suggesting limits to the effectiveness of crowdsourcing approaches. In this paper, we assess the viability of crowdsourcing privacy policy annotations. Our results suggest that, if carefully deployed, crowdsourcing can indeed result in the generation of non-Trivial annotations and can also help identify elements of ambiguity in policies. We further introduce and evaluate a method to improve the annotation process by predicting and highlighting paragraphs relevant to specific data practices.

Publication Date

1-1-2016

Publication Title

25th International World Wide Web Conference, WWW 2016

Number of Pages

133-143

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/2872427.2883035

Socpus ID

85025813224 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85025813224

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