Privacy & Security in Human-Computer Interfaces

In the age of wearables, virtual and augmented reality, our personal information is continuously collected while our perception of the world is prone to manipulation. Security and privacy must be at the forefront of the design of any human-computer interfaces. In this research cluster, we have studied a number of security and privacy issues for a number of HCI devices from bodycam to virtual reality systems. We found that these systems introduce new cyberphysical security challenges but also open new doors for better privacy and security countermeasures.

Cybersecurity of VR Systems

Virtual Reality (VR) is a rapidly advancing technology with diverse applications. VR systems offer an immersive, life-like virtual experience by rendering interactive views on a head-mounted display (HMD). VR systems are vulnerable to various types of attacks, which could have dire consequences as they are designed to replace our perception of the physical world. However, there have been few studies on the security and integrity issues of the VR systems. We focus on the popular HTC Vive VR system and propose novel attack methods on blocking and manipulating its tracking subsystem. It is shown that simple attacks can jam or even manipulate the entire position and pose tracking process. Possible countermeasures are suggested to make VR systems safer and more secure.

The blue box is an attack device that senses and manipulates infra-red light used by the VR system for tracking the user's head and hand positions.

Our simple device can completely take over the tracking of the VR system. The above figures show the 3D position (X,Y,Z) and orientation (Roll, Pitch, Yaw) of a completely stationary headset. An attack is launched around t=1000 to move the location and orientation to a different pose.

Visual Bubble - Wearable Privacy Camera

Wearable cameras are being used more frequently in many different consumer applications, including entertainment, law enforcement, and healthcare. To protect the privacy of the environment and bystanders, we introduce a new visualprivacy paradigm called the visual bubble. In contrast to most existing visual privacy schemes, the visual bubble is based on depth estimation to determine the extent of privacy protection. To demonstrate this concept, we built a wearable privacy stereo-camera system using the Raspberry Pi platform. Our system takes into account the uncertainty of stereo-depth measurement in a privacy risk-minimization framework and combines with a super pixel based disparity postprocessing step to improve disparity measurements.

Selected Publications:

Rafique, M. U., and S.-C. Cheung. 2018. Tracking Attacks on Virtual Reality Systems. IEEE Consumer Electronics Magazine. (Accepted on 9/21/2018)
Wang, S., S.-C. Cheung, and H. Sajid. 2018. Visual Bubble: Protecting Privacy with Wearable Cameras. IEEE Consumer Electronics Magazine. Vol. 7, Issue 1, pp. 95-105, Jan. 2018.
Wang, S., S.-C. Cheung, and Y. Luo. 2016. Wearable Privacy Protection with Visual Bubble. In 1st IEEE International Workshop on Privacy Issues in Multimedia at IEEE International Conference on Multimedia Expo (ICME 2016), 15 July 2016, Seattle, WA.
Sajid, H. and S.-C. Cheung. 2015. VSig: Hand-Gestured Signature Recognition and Authentication with Wearable Camera. In IEEE International Workshop on Information Forensics and Security (WIFS 2015), 16-19 November 2015, Rome, Italy.

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