Towards Effective Differential Privacy Communication for Users' Data Sharing Decision and Comprehension
This research addresses the problem of effectively communicating privacy techniques to users for better decision-making, though it is incremental as it builds on existing DP/LDP frameworks with human-subject experiments.
The study investigated how different communication approaches for differential privacy (DP) and local differential privacy (LDP) affect laypersons' data sharing decisions and comprehension in a health app setting, finding that descriptions explaining implications rather than definitions improved comprehension and increased willingness to share with LDP over DP.
Differential privacy protects an individual's privacy by perturbing data on an aggregated level (DP) or individual level (LDP). We report four online human-subject experiments investigating the effects of using different approaches to communicate differential privacy techniques to laypersons in a health app data collection setting. Experiments 1 and 2 investigated participants' data disclosure decisions for low-sensitive and high-sensitive personal information when given different DP or LDP descriptions. Experiments 3 and 4 uncovered reasons behind participants' data sharing decisions, and examined participants' subjective and objective comprehensions of these DP or LDP descriptions. When shown descriptions that explain the implications instead of the definition/processes of DP or LDP technique, participants demonstrated better comprehension and showed more willingness to share information with LDP than with DP, indicating their understanding of LDP's stronger privacy guarantee compared with DP.