Anonymizer: Making Sensitive Data Available for Distribution

Scott Gangloff

3rd place winner at CCSC SE 2020 Student Research Contest: see pp. 21-22: http://www.ccscse.org/research_contest/Addendum2020.pdf

Data privacy is an important policy that is necessary to keep personal information from unauthorized access. Because of this, data is often kept locked, is available to only certain individuals, or can never be shared even when there are benefits of sharing without personally identifiable information (PII). Obtaining datasets for research is often hard to do. This can be an issue when an organization tries to research on datasets that include PII, and it frequently happens at Lander University’s Institutional Research when they research Lander University’s student datasets. A group of student developers could not work on dashboards and presentations with the student datasets due to their lack of security clearance. To solve this issue, we developed a tool that reads a data file, anonymizes certain sensitive data fields, and exports a resulting file that can be distributed and shared without worrying about security clearance. This tool, called the ‘Anonymizer’, allows users to import a CSV (Comma-Separated Values) data file, specify what features should be anonymized, the type of data each feature is, and relations between other features.

• Scott Gangloff graduated from Sweet Home High School in Buffalo, New York. He is currently a senior in Computer Information Systems with an emphasis on software development. His experiences include working as a student research intern for Matt Braaten at Lander University and presenting at the CCSC : SE student research competition.