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Privacy/Disaster: When Information Flows Are Taken Out of Context
Madelyn R. Sanfilippo & Yan Shvartzshnaider

Princeton University | New York University

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Privacy/Disaster: When Information Flows Are Taken Out of Context

Abstract

Privacy is contextual. Everyday, we manage different contexts and adjust our privacy expectations accordingly. The theory of Contextual Integrity offers a way to capture contextual norms and a heuristic to analyze privacy. This analysis is especially helpful to detect situations in which the system designers take advantage of well-established, contextual privacy expectations, to encourage user disclosures without adhering to governing norms. For example, imagine an app that is marked to you as a patient/doctor communication tool in a medical context, yet it is actually an insurance company trying to get more information on you. Sounds too improbable? Think about the context of emergencies. When a natural disaster hits, it not only wreaks havoc to our infrastructure but also to our expectations of privacy. Privacy expectations during disasters differ significantly from non-emergency situations. Individuals are much more willing to share personal information, but also have high expectations about appropriate behavior by information recipients. Increased use of social technologies to facilitate communication and support first responders provide more opportunities for privacy infringements, despite increased regulation of disaster information flows to government agencies and with trusted partners of the government. In this talk, we will discuss our work exploring the actual practices followed by popular disaster apps.

Biography

Madelyn Rose Sanfilippo is a postdoctoral research associate at the Center for Information Technology Policy (CITP) at Princeton University. She studies governance of sociotechnical systems, exploring collaborative governance arrangements around data and knowledge, including social justice issues. Madelyn studied at the University of Wisconsin, Madison as an undergraduate and completed her masters and doctoral studies in information science at Indiana University, Bloomington’s School of Informatics and Computing. Madelyn was previously a postdoctoral research scholar at the Information Law Institute at New York University, where she studied privacy and knowledge commons governance.

Yan Shvartzshnaider is an Assistant Professor/Faculty Fellow in the Courant Institute of Mathematical Sciences at NYU, where he is affiliated with Analysis of Computer Systems (ACSys) and Open Networks and Big Data Lab groups. He is also a Visiting Associate Research Scholar at Center for Information Technology Policy (CITP) in Princeton University. Yan is working on architectures and algorithms for privacy-preserving information systems. His research interests are focused on people-centered privacy design which calls for the adoption of a socially meaningful conception of privacy, namely, one that meets people’s expectations of privacy.

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