publication . Conference object . 2019

student research abstract

Majid Hatamian;
Open Access English
  • Published: 08 Apr 2019
  • Publisher: ACM
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...
Persistent Identifiers
free text keywords: Data access, Privacy policy, Computer science, Supervised learning, Declaration, User interface, End user, Permission, Data Protection Act 1998, World Wide Web
Related Organizations
Funded by
EC| Privacy.Us
Privacy and Usability
  • Funder: European Commission (EC)
  • Project Code: 675730
  • Funding stream: H2020 | MSCA-ITN-ETN
Validated by funder
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