Restricted (Penn State Only)
Aurite, William Robert
Graduate Program:
Information Sciences and Technology
Master of Science
Document Type:
Master Thesis
Date of Defense:
April 11, 2017
Committee Members:
  • Dr. Andrea Tapia, Thesis Advisor
  • Dr. Jessica Kropczynski , Committee Member
  • Dr. Jens Grossklags, Committee Member
  • Genetic Data
  • Genetic Privacy
  • Interdependent Privacy
  • Data Sharing With Third Parties
  • Personal Privacy
Direct-to-consumer genetic testing services have expanded alongside the proliferation of the Web. Greatly simplified access to the Web allows consumers of these services to receive detailed, personalized reports about their ancestry, health, phenotypic and genotypic information. In addition to determining the test-taker’s genetic makeup, genetic details of the test-taker’s family members are also indirectly revealed through direct-to-consumer genetic testing. As such, taking a genetic test contains personal and interdependent privacy considerations, considerations that serve as the main motivation for this thesis. We find that these considerations play important roles in genetic test-taking service adoption, genetic test-taking service recommendation, and trust in organizations or institutions receiving test-taker data. We conduct two studies using the methodology of factorial vignette surveys. In study one, we assess how attitudes and perceptions of interdependency influence genetic test service adoption. Specifically, we examine the factors that make someone more or less likely to take a genetic test, along with the factors that make an individual more or less likely to recommend a test. Additionally, we judge to which degree psychological factors influence stated adoption choices and privacy concerns by studying the influence of different thinking styles (construal level). In study two, we investigate how variables of ethnicity, age, genetic markers, and association of data with the individual’s name affect the likelihood of sharing data with different types of organizations. We also investigate elements of personal and interdependent privacy concerns. We document the significant role these factors have in the decision to share or not share genetic data with a third party. We also propose a deterministic model that accounts for differences in sharing preferences among individuals who share data with academic, medical, or governmental organizations.