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Massive amounts of data are being collected, stored, and analyzed for various business and marketing purposes. While such data analysis is critical for many applications, it could also violate the privacy of individuals. This paper describes the issues involved in designing a privacy aware data management framework for collecting, storing, and analyzing the data. We also discuss behavioral aspects of data sharing as well as aspects of a formal framework based on rewriting rules that encompasses the privacy aware data management framework.

There is an increasing need for methods for secure dissemination of interactive 3D graphics content, providing protection for valuable 3D models while still allowing them to be widely shared. Existing systems for protected sharing of 3D models may introduce perturbations into the rendered images of the content, in order to defend against potential malicious reconstruction attacks that could otherwise recover the 3D model shape. However, the particular nature and magnitude of these perturbation defenses has not been based upon any rigorous analysis or measurement of their perceptual effect on non-malicious users of the protected graphics system. In this paper, we take the first steps toward such an analysis, conducting a series of user studies that evaluate the impact (as measured by user reaction time) of varying amounts of noise applied to user interactions in a real-time 3D rendering system. We are thus able to experimentally determine the most appropriate tradeoffs between noise perturbation defenses and the security of the 3D content against typical reconstruction attacks.

Previous research has found performance for several egocentric tasks to be superior on physically large displays relative to smaller ones, even when visual angle is held constant. This finding is believed to be due to the more immersive nature of large displays. In our experiment, we examined if using a large display to learn a virtual environment (VE) would improve egocentric knowledge of the target locations. Participants learned the location of five targets by freely exploring a desktop large-scale VE of a city on either a small (25" diagonally) or large (72" diagonally) screen. Viewing distance was adjusted so that both displays subtended the same viewing angle. Knowledge of the environment was then assessed using a head-mounted display in virtual reality, by asking participants to stand at each target and paint at the other unseen targets. Angular pointing error was significantly lower when the environment was learned on a 72" display. Our results suggest that large displays are superior for learning a virtual environment and the advantages of learning an environment on a large display may transfer to navigation in the real world.