Project Synopsis

Image data is being largely captured by ubiquitous sensing cameras, such as traffic cameras, security surveillance cameras, and body-worn cameras. The publication of image data would greatly benefit the research communities and enable many applications, including the study of driving behaviors toward intelligent transportation systems and the study of building access patterns to improve energy efficiency. However, the publication of high resolution images would raise privacy concerns due to sensitive content captured by the cameras, e.g., identities and activities. Prior to sharing with untrusted parties, the image data must be sanitized to protect the privacy of persons and objects represented in the data.

Recent research studies have shown that images obfuscated via standard obfuscation, such as pixelization and bluring, can be recovered and re-identified (McPherson et al. 2016, Hill et al. 2016). The goal of this project is to develop image data publication solutions with a rigorous privacy notion, i.e., differential privacy, to enable data sharing without compromising privacy.

Methodology

We propose to adapt the standard notion of differential privacy to image data and aim to develop high utility image obfuscation/sampling algorithms with the rigorous privacy guarantees. The output image, e.g., below on right, should be compatible with existing image processing techniques for accurate extraction of useful information, e.g., blobs detected in green boxes. More technical details can be found in our publications.

Left: Input image; Right: Privacy-enhanced output image

People

Faculty: Dr. Liyue Fan - Assistant Professor at UNCC

Undergraduate Student: Zachary McVicker (UAlbany, graduated May 2018, now at [s]Cube)

Graduate Student: Monali Vithalani (UAlbany, graduated May 2019, now at Software Built by Design), Abhilash Mandlekar (UNCC, current MS in Computer Science), Jeet Jivrajani (UNCC, current MS in Computer Science).

Publications

  1. Liyue Fan. Practical Image Obfuscation with Provable Privacy. Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), 2019. Link
  2. Liyue Fan. Differential Privacy for Image Publication. The 2019 Theory and Practice of Differential Privacy Workshop (TPDP), 2019. Link
  3. Liyue Fan. Image Pixelization with Differential Privacy. Proceedings of the 32nd Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy (DBSec), 2018. Link

Resources

Public Datasets

  1. UCSD Pedestrian Traffic Database
  2. MOT 2016 Benchmark

Code Repositories