Automatic extraction of opt-out choices from privacy policies

Kanthashree Mysore Sathyendra, Florian Schaub, Shomir Wilson, Norman Sadeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Online "notice and choice" is an essential concept in the US FTC's Fair Information Practice Principles. Privacy laws based on these principles include requirements for providing notice about data practices and allowing individuals to exercise control over those practices. Internet users need control over privacy, but their options are hidden in long privacy policies which are cumbersome to read and understand. In this paper, we describe several approaches to automatically extract choice instances from privacy policy documents using natural language processing and machine learning techniques. We define a choice instance as a statement in a privacy policy that indicates the user has discretion over the collection, use, sharing, or retention of their data. We describe supervised machine learning approaches for automatically extracting instances containing opt-out hyperlinks and evaluate the proposed methods using the OPP-115 Corpus, a dataset of annotated privacy policies. Extracting information about privacy choices and controls enables the development of concise and usable interfaces to help Internet users better understand the choices offered by online services. The focus of this paper, however, is to describe such methods to automatically extract useful opt-out hyperlinks from privacy policies.

Original languageEnglish (US)
Title of host publicationFS-16-01
Subtitle of host publicationArtificial Intelligence for Human-Robot Interaction; FS-16-02: Cognitive Assistance in Government and Public Sector Applications; FS-16-03: Cross-Disciplinary Challenges for Autonomous Systems; FS-16-04: Privacy and Language Technologies; FS-16-05: Shared Autonomy in Research and Practice
PublisherAI Access Foundation
Pages270-275
Number of pages6
ISBN (Electronic)9781577357759
StatePublished - Jan 1 2016
Event2016 AAAI Fall Symposium - Arlington, United States
Duration: Nov 17 2016Nov 19 2016

Publication series

NameAAAI Fall Symposium - Technical Report
VolumeFS-16-01 - FS-16-05

Conference

Conference2016 AAAI Fall Symposium
CountryUnited States
CityArlington
Period11/17/1611/19/16

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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