Balancing Privacy and Information Disclosure in Interactive Record Linkage with Visual Masking
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abstract
Copyright 2018 ACM. Effective use of data involving personal or sensitive information often requires different people to have access to personal information, which significantly reduces the personal privacy of those whose data is stored and increases risk of identity theft, data leaks, or social engineering attacks. Our research studies the tradeoffs between privacy and utility of personal information for human decision making. Using a record-linkage scenario, this paper presents a controlled study of how varying degrees of information availability influences the ability to effectively use personal information. We compared the quality of human decision-making using a visual interface that controls the amount of personal information available using visual markup to highlight data discrepancies. With this interface, study participants who viewed only 30% of data content had decision quality similar to those who had full 100% access. The results demonstrate that it is possible to greatly limit the amount of personal information available to human decision makers without negatively affecting utility or human effectiveness. However, the findings also show there is a limit to how much data can be hidden before negatively influencing the quality of judgment in decisions involving person-level data. Despite the reduced accuracy with extreme data hiding, the study demonstrates that with proper interface designs, many correct decisions can be made with even legally de-identified data that is fully masked (74.5% accuracy with fully-masked data compared to 84.1% with full access). Thus, when legal requirements only allow for deidentified data access, use of well-designed interface can significantly improve data utility.
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Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
