Conference Proceeding Article
While mobile platforms rely on developers to follow good practices in privacy design, developers might not always adhere. In addition, it is often difficult for users to understand the privacy behaviour of their applications without some prolonged usage. To aid in these issues, we describe on-going research to improve privacy protection by utilizing techniques that mine privacy information from application binaries as a grey-box (Automated Privacy Checking). The outputs can then be utilized to improve the users' ability to exercise privacy-motivated discretion. We conducted a user study to observe the effects of presenting information on leak-causing triggers within applications in the form of privacy message overlays. We found that while users' prior usage time largely determined their usage behaviour, presenting trigger information helped users who disapproved with data use and had sufficient understanding of the implications of data leaks. Users' inherent level of privacy consciousness and surprise levels were also factors in ensuring the effectiveness of messages.
mobile privacy, binary analysis, user-behavioural factors
Information Security | Software Engineering
Software and Cyber-Physical Systems
CHI 2016: The 34th Annual CHI Conference on Human Factors in Computing Systems: San Jose, CA, May 7-12
City or Country
CHAN, Joseph Joo Keng; JIANG, Lingxiao; TAN, Kiat Wee; and BALAN, Rajesh.
Leveraging automated privacy checking for design of mobile privacy protection mechanisms. (2016). CHI 2016: The 34th Annual CHI Conference on Human Factors in Computing Systems: San Jose, CA, May 7-12. 1-4. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3513
Copyright Owner and License
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.