publication . Conference object . 2019

student research abstract

Majid Hatamian;
Open Access English
  • Published: 01 Apr 2019
Abstract
In this paper, we propose a novel automated approach to predict the potential privacy sensitive permission requests by mobile apps. Based on machine learning (ML) and natural language processing (NLP) techniques, personal data access and collection practices mentioned in app privacy policy text are analyzed to predict the required permission requests. Further, the predicted list of permission requests is compared with the real permission requests to check whether there is any mismatch. We further propose user interface designs to map mobile app permission requests to understandable language definitions for the end user. The combination of these concepts provides...
Subjects
free text keywords: Privacy policy, Supervised learning, Declaration, User interface, Computer science, Permission, End user, Data Protection Act 1998, Data access, World Wide Web
Related Organizations
Funded by
EC| Privacy.Us
Project
Privacy.Us
Privacy and Usability
  • Funder: European Commission (EC)
  • Project Code: 675730
  • Funding stream: H2020 | MSCA-ITN-ETN
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publication . Conference object . 2019

student research abstract

Majid Hatamian;