Trust when Sharing Data Online
Decisions about confidentiality protection measures to be applied to data dissemination must be informed by evidence about the utility associated with the quality of the data and the willingness to trade utility against the estimated risk. Doing so requires measurement of data utility, risk, and the willingness of individuals to trade risk for utility. From the theoretical literature on measuring privacy (Nissenbaum 2011) and trust (Bauer and Freitag 2018), perceptions of trust and privacy are context dependent. There are three dimensions that are particular important: (1) to whom the data is provided, (2) what is done with the data (i.e., whether there are benefits for the one receiving the data vs. benefits for the one providing the data), and (3) what kind of data is shared (i.e., the sensitivity of the data). Some data are inherently sensitive because they touch taboo topics (e.g., information on income, sexual behavior, etc.), other data is only sensitive if it reveals specific information about illegal (e.g., illicit drug use) or counter-normative behaviors and attitudes (Tourangeau and Yan 2007). In this project, we measure utility, risk, and tradeoffs in the context of privacy and data sharing in several cross-sectional surveys. The data landscape has dramatically changed in May of 2018 when GDPR came into effect, and with it the control people have about their data, and the risks companies face when violating GDPR. Thus, we also collect longitudinal data on the awareness about the GDPR regulations in Germany, and in an experimental setting, we measure the influence of GDPR information on trust in various data collecting organizations.