Towards a Triad for Data Privacy

Data privacy is a topic of interest for researchers, data collection managers, and data system specialists. In an attempt to assuage growing concerns regarding the collection and use of personal data, many organizations have begun developing systems and drafting policies meant to safeguard that data from potential privacy harms. This paper provides a surface-level comparison of data privacy triads from NIST in the United States and ULD in Germany that may form the basis for a future universal definition of data privacy. The analysis shows two different approaches for defining data privacy: one which focuses on the practical implementation of data privacy safeguards (NIST) and one that focuses on defining the highest possible standards to which data processors must be held (ULD)

An Inventory of International Privacy Principles: A 14 Country Analysis

Companies are operating within a global marketplace where they must navigate differing laws related to data privacy, so it is important to understand and respect the privacy concerns of various countries. To that end, this paper will provide an inventory of the data privacy principles set out by fourteen countries around the world. By looking at the similarities and differences between nations, it is possible to work toward a common understanding and agreement of which principles should be approved and thereafter enforced. With technology evolving so rapidly, laws cannot wait to be reactionary; rather the development of privacy principles can be used to guide future implementation of regulation

Student Privacy and Learning Analytics: Investigating the Application of Privacy within a Student Success Information System in Higher Education

Learning analytics are starting to become standardized in higher education as institutions use the techniques of Big Data analytics to make decisions to help them reach their goals. The widespread use of student information brings forth ethical concerns primarily in relation to privacy. While the overarching ethical issues related to learning analytics are discussed in the literature, there has been a call for more studies to examine how they are put into practice. This case study used interviews and other data resources to determine how privacy is addressed within a student success information system at a public institution of higher education. During the inductive coding process three main themes emerged related to the connection between FERPA and privacy, methods to maintain privacy, and students’ connection with their data. A deductive coding process was also undertaken to determine how the institution addressed the privacy principles put forth in the larger privacy literature. Overall, the findings showed the institution had a minimal understanding of privacy concerns related to learning analytics. This was not unexpected given the length of time the system had been in use at the institution. Recommendations for the institution include developing policies and procedures to guide their use of learning analytics moving forward