  In recent years, the amount of information collected about human beings has
increased dramatically. This development has been partially driven by
individuals posting and storing data about themselves and friends using online
social networks or collecting their data for self-tracking purposes
(quantified-self movement). Across the sciences, researchers conduct studies
collecting data with an unprecedented resolution and scale. Using computational
power combined with mathematical models, such rich datasets can be mined to
infer underlying patterns, thereby providing insights into human nature. Much
of the data collected is sensitive. It is private in the sense that most
individuals would feel uncomfortable sharing their collected personal data
publicly. For this reason, the need for solutions to ensure the privacy of the
individuals generating data has grown alongside the data collection efforts.
Out of all the massive data collection efforts, this paper focuses on efforts
directly instrumenting human behavior, and notes that -- in many cases -- the
privacy of participants is not sufficiently addressed. For example, study
purposes are often not explicit, informed consent is ill-defined, and security
and sharing protocols are only partially disclosed. This paper provides a
survey of the work related to addressing privacy issues in research studies
that collect detailed sensor data on human behavior. Reflections on the key
problems and recommendations for future work are included. We hope the overview
of the privacy-related practices in massive data collection studies can be used
as a frame of reference for practitioners in the field. Although focused on
data collection in an academic context, we believe that many of the challenges
and solutions we identify are also relevant and useful for other domains where
massive data collection takes place, including businesses and governments.
