Owners of data may wish to share some statistics with others, but they may be worried of privacy of the underlying data. An effective solution to this problem is to employ provable privacy techniques, such as differential privacy, to add noise to the statistics before releasing them. This protection lowers the risk of sharing sensitive data with more or less trusted data sharing partners. Unfortunately, applying differential privacy in its mathematical form requires one to fix certain numeric parameters, which involves subtle computations and expert knowledge that the data owners may lack.In this paper, we first describe a differential privacy parameter selection procedure that minimizes what lay data owners need to know. Second, we describe a user visualization and workflow that makes this procedure available for lay data owners by helping them set the level of noise appropriately to achieve a tolerable risk level. Finally, we describe a user study in which human factors professionals who were native to differential privacy were briefly trained on the concept of using differential privacy for data sharing and then used the visualization to determine an appropriate level of noise.

The Defense Advance Research Projects Agency Augmented Cognition program is developing innovative technologies that will transform human-computer interactions by making information systems adapt to the changing capabilities and limitations of the user. The first phase of the Augmented Cognition program was to empirically assess the ability of various psychophysiological measures to identify changes in human cognitive activity during task performance in real time. This overview describes the empirical results of a Technical Integration Experiment involving the evaluation of 20 psychophysiological measures from 11 different research groups, including functional Near Infrared imaging, continuous and event-related electrical encephalography, pupil dilation, mouse pressure, body posture, heart rate, and galvanic skin response. These "cognitive state gauges" were evaluated on a common, quasi-realistic, military command and control task called the Warship Commander Task. Participants monitored aircraft on a geographical display for their levels of threat and responded to the threatening ones, as they simultaneously monitored ship communications for ship status information. The task involves a combination of perceptual, motor, spatial, auditory, verbal, memory, and decision-making processing. Task load was manipulated by changing the quantity and types of aircraft appearing throughout the primary task and by varying the presence or absence of the secondary verbal-memory task. Eleven of the gauges significantly identified changes in cognitive activity during the task. This overview summarizes the results and examines the prospects for the successful transition of these cognitive state gauges to operational military human-machine systems.