Enhancing Privacy through an Interactive On-demand Incremental Information Disclosure Interface: Applying Privacy-by-Design to Record Linkage
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Presented at SOUPS2019 2019 by
Achieving the benefits of data science in cases involving personal data requires the use of that data, which results in some privacy risk. Our research investigates approach-es to enhance privacy while supporting legitimate access for human decision making by capitalizing on the fact that in most human-computer hybrid systems, only a small fraction of the full data is required for human judgment. We present an interactive visual system for record linkage – a task that requires human decision-making about whether different but similar data records refer to the same person. The system employs an on-demand interactive interface that incrementally discloses partial in-formation only when needed and other feedback mechanisms to promote ethical behavior. We evaluate our approach with a controlled experiment of how different types of feedback and access restrictions affect human decision-making quality, speed, and access behavior. The on-demand interactive interface reduced privacy risk to only 7.85%, compared to 100% when all data is disclosed, with little to no impact on decision quality or completion time. In addition, feedback from an expert review supports the notion that an intermediate level of access other than “all or nothing” can provide better accuracy than no access but more protection than full access.