Visual Privacy

With the growth and accessibility of mobile devices and internet, the ease of posting and sharing content on social media networks (SMNs) has increased exponentially. Privacy leaks include any instance in which a transfer of personal identifying visual content is shared on SMNs. Private visual content (images and videos) exposes intimate information that can be detrimental to your finances, personal life, and reputation. Private visual content can include baby faces, credit cards, social security cards, house keys and others. Any content posted to social media networks (SMNs) can be lost to someone else even after removal of the content. Stolen visual content can then be used as a transport vector for other types of cyber-attacks or social engineering.

We investigate (1) how pervasive social media-based privacy visual content leaks are and (2) what reasonable mitigation strategies can be developed to detect and minimize these leaks. We use deep learning techniques to identify “private” information.Mitigation Techniques include:

  1. Client side. Users will download a third-party application to be installed with various SMN applications on electronics to prevent posting of potential leaks. This third-party application will pre-screen content before it can be posted on SMNs.
  2. Privacy Patrol. This is a crawler that will randomly look at users’ pages, screening for privacy leaks and alerting users of various potential leaks.
  3. Chaperone bot. Users can add a chaperone bot as a friend on SMNs. The chaperone bot will give users friendly suggestions based on type and frequency of privacy leaks on SMNs.
  4. Category Tag. Users select the category that the content belongs to before being uploaded to SMNs. Once tagged, an automated system will check for content compliance with tag. If it does not fit the category, the user is notified of what category tag the image should have.
  5. Privacy Score. Users will be monitored based on privacy score given by bots. The bot will check the users’ content after posting to remove any leaks.
  6. Server side. The SMN will screen visual content before uploading to platform. We suggest collaboration with SMNs to provide enforcement of user compliance and techniques.
  7. Interception. With the SMN applications, users will agree to let the SMN intercept the camera and gallery to flag and block content that should not be selected for posting.

Publications

Jasmine DeHart, Christan Grant. Visual Content Privacy Leaks on Social Media Networks. The 39th IEEE Symposium on Security and Privacy (S&P). San Francisco, California. 2018.