The Privacy Paradox and Optimal Bias-Variance Trade-offs in Data Acquisition
The Privacy Paradox and Optimal Bias-Variance Trade-offs in Data Acquisition
复制标题
数据采集中的隐私悖论和最优偏差-方差权衡
DOI：
10.1145/3512798.3512802
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Jianwei Huang
中科院分区：
文献类型：
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作者：
Guocheng Liao;Yu Su;Juba Ziani;A. Wierman;Jianwei Huang
尽管用户声称关心隐私，但他们通常很少采取措施保护在线行为中的隐私。对这种“隐私悖论”的一个突出解释是，当一个人分享她的数据时，不仅她的隐私受到损害，她的隐私也会受到损害。拥有相关数据的其他个人的隐私也受到损害。这种信息泄露鼓励了数据的过度共享，并严重影响了在线平台中个人的积极性。在这个扩展摘要中，我们讨论了在存在信息泄漏和可验证数据的情况下数据采集机制的设计。我们总结了设计一种激励兼容机制的工作，该机制在预算约束下优化估计偏差和方差之间的最坏情况权衡，其中最坏情况是成本和数据之间未知的相关性。此外，我们以封闭形式描述了最优机制的结构，并研究了市场的单调性和非单调性。
While users claim to be concerned about privacy, often they do little to protect their privacy in their online actions. One prominent explanation for this "privacy paradox" is that when an individual shares her data, it is not just her privacy that is compromised; the privacy of other individuals with correlated data is also compromised. This information leakage encourages oversharing of data and significantly impacts the incentives of individuals in online platforms. In this extended abstract, we discuss the design of mechanisms for data acquisition in settings with information leakage and verifiable data. We summarize work designing an incentive compatible mechanism that optimizes the worst-case tradeoff between bias and variance of the estimation subject to a budget constraint, where the worst-case is over the unknown correlation between costs and data. Additionally, we characterize the structure of the optimal mechanism in closed form and study monotonicity and non-monotonicity properties of the marketplace.
无需先验的数据采集，可进行准确的统计估计
DOI：
10.1145/3328526.3329564
发表时间：
2019
期刊：
Proceedings of the 2019 ACM Conference on Economics and Computation
影响因子：
--
作者：
Chen, Yiling;Zheng, Shuran
通讯作者：
Zheng, Shuran
用于统计估计的最佳数据采集
DOI：
10.1145/3219166.3219195
发表时间：
2018
期刊：
ACM Conference on Economics and Computation
影响因子：
--
作者：
Chen, Yiling;Immorlica, Nicole;Lucier, Brendan;Syrgkanis, Vasilis;Ziani, Juba
通讯作者：
Ziani, Juba