How does Government surveillance affect perceived online privacy/security and online information disclosure?

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• Laura Brandimarte (University of Arizona)
• Friday 28 October 2016, 10:00-11:00
• Seminar Room 1, Newton Institute.

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DLAW04 - Privacy: recent developments at the interface between economics and computer science

Disclosure behaviors in the digital world are affected by perceived privacy and security just as much, or arguably more than they are by actual privacy/security features of the digital environment. Several Governments have recently been at the center of attention for secret surveillance programs that have affected the sense of privacy and security people experience online. In this talk, I will discuss evidence from two research projects showing how privacy concerns and disclosure behaviors are affected by perceived privacy/security intrusions associated with Government monitoring and surveillance. These two interdisciplinary projects bring together methodologies from different disciplines: information systems, machine learning, psychology, and economics.

The first project is in collaboration with the Census Bureau, and studies geo-location and its effects on willingness to disclose personal information. The U.S. Census Bureau has begun a transition from a paper-based questionnaire to an Internet-based one. Online data collection would not only allow for a more efficient gathering of information; it would also, through geo-location technologies, allow for the automated inference of the location from which the citizen is responding. Geo-location features in Census forms, however, may raise privacy concerns and even backfire, as they allow for the collection of a sensitive piece of information without explicit consent of the individual. Four online experiments investigate individuals’ reactions to geo-location by measuring willingness to disclose personal information as a function of geo-location awareness and the entity requesting information: research or Governmental institutions. The experiments also explicitly test how surveillance primes affect the relationship between geo-location awareness and disclosure. Consistent with theories of perceived risk, contextual integrity, and fairness in social exchanges, we find that awareness of geo-location increases privacy concerns and perceived sensitivity of requested information, thus decreasing willingness to disclose sensitive information, especially when participants did not have a prior expectation that the institution would collect that data. No significant interaction effects are found for a surveillance prime.

The second project is ongoing research about the “chilling effects” of Government surveillance on social media disclosures, or the tendency to self-censor in order to cope with mass monitoring systems raising privacy concerns. Until now, such effects have only been estimated using either Google/Bing search terms, Wikipedia articles, or survey data. In this research in progress, we propose a new method in order to test for chilling effects in online social media platforms. We use a unique, large dataset of Tweets and propose the use of new statistical machine learning techniques in order to detect anomalous trends in user behavior (use of predetermined, sensitive sets of keywords) after Snowden’s revelations made users aware of existing surveillance programs.

This talk is part of the Isaac Newton Institute Seminar Series series.

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