Investigating the impact of differential privacy obfuscation on users’ data disclosure decisions
通过两个实验（N1=588, N2=146）模拟数据市场，发现个人报酬和差分隐私保护水平对用户参与数据收集的决策影响最大，而数据类型的披露影响最小；提高一个单位的保护水平可使用户要求的补偿价格降低60%以上。
Differential Privacy (DP) has emerged as the standard for privacy-preserving analysis of individual-level data. Despite growing attention in the research community to the operationalization of DP through the selection of the privacy budget ɛ , little is known about how DP obfuscation affects users’ disclosure decisions in data market scenarios. These decisions may be context-specific and vary with privacy preferences, eliciting disparate data valuations across individuals. Through a choice-based conjoint analysis ( N 1 = 588 ), simulating realistic data markets, we analyzed how varying DP protection levels influence individual decision-making of participation in data collection under DP. Our findings show that personal reward and the guaranteed DP protection had the strongest influence on participants’ selection of a data collection scenario. Surprisingly, the type of disclosed data had the least influence on participants’ decisions to disclose personal data, a trend consistent across participants from different countries. Furthermore, increasing the DP protection level by a single unit reduced the preferred compensation price by over 60% for the same level of user utility, with marginal effects diminishing exponentially at higher DP levels. Our results were then confirmed in an online study ( N 2 = 146 ) involving real data disclosure with actual payments, using our original scenario framing. Our findings can support context-specific DP configuration and help data practitioners improve decision-making associated with privacy protection in differentially private systems, balancing the trade-off between DP and compensation costs.