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Respecting Context to Study Privacy: the Good, the Bad, and the Ugly

Date:2019-05-27    From:

Topic: Respecting Context to Study Privacy: the Good, the Bad, and the Ugly

Speaker: Heng Xu

Time: June 62019  15:00

Location: No. 25 teaching building, 3th floor, A classroom

 

Speaker:




Dr. Heng Xu is a Professor of IT & Analytics at the American University’s Kogod School of Business, where she also serves as the Director for the Kogod Cyber security Governance Center. Before joining Kogod, she had a mix of academic and government background, being a professor at Penn State for 12 years, as well as a program director at the U.S. National Science Foundation (NSF) for 3 years. Dr. Xu's current research focus is on information privacy, data ethics, and data analytics. Her work has received many awards, including the NSF CAREER award in 2010, the Operational Research Society’s Stafford Beer Medal in 2018, and a total of 10 best paper awards and nominations at various leading research conferences. Currently she is an associate editor at Management Information Systems Quarterly.

 

Abstract:

As our information ecology evolves to be more digital and ubiquitous, we are moving from a world where data collection and processing is siloed and specialized, to a world where everyday individuals produce massive trails of data and consume considerable amounts of data products. When this complex ecosystem is forming around individuals, organizations, and data, it is important to understand how the human-technology frontier is shaping up in data practices to balance between technological design and the human/societal needs. A major aspect of this human-technology frontier is the concerns on information privacy, which is becoming an increasingly critical and global challenge for many stakeholders including business leaders, IT professionals, privacy activists, and government regulators. In this talk, I will first describe how extensive research efforts were spent on refining the highly contextualized nature of privacy, and revealing factors that affect people's privacy concerns, from cognition to emotion to environment. Then I will argue that, while we can continue down this line to keep adding factors towards an overcomplicated model of privacy: the more researchers try to “refine” a construct to precision, the easier it is for them to focus too much on “siloed” factors and lose sight of the “big picture”, eventually departing from what real-world people think the construct means. What I propose in my ongoing work is to develop practical models that are not designed to serve as a comprehensive explanation for the construct of privacy, but to capture most concerns by most individuals in most situations, towards the goal of actionable design guidelines for more usable privacy solutions in practice.

 

 

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