CrowdMask: Using Crowds to Preserve Privacy in Crowd-Powered Systems via Progressive Filtering
CrowdMask: Using Crowds to Preserve Privacy in Crowd-Powered Systems via Progressive Filtering
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CrowdMask：通过渐进式过滤，利用群体在群体驱动的系统中保护隐私
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Walter S. Lasecki
中科院分区：
文献类型：
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作者：
Harmanpreet Kaur;Mitchell L. Gordon;Yi Wei Yang;Jeffrey P. Bigham;J. Teevan;Ece Kamar;Walter S. Lasecki
群体驱动的系统利用人类智能超越了自动化系统的能力，但也引入了隐私和安全问题，因为未知的人必须查看系统处理的数据。虽然自动化方法无法从这些数据集中稳健地过滤私人信息，但如果可以减轻查看数据的风险，人们就有能力这样做。我们提出了一种群体驱动的方法来屏蔽数据中的私人内容，通过分割和分发较小的片段给人群工作人员，使个人工作人员可以识别潜在的私人内容，而无需完全查看它自己。我们引入了一种新的金字塔分割工作流程，使用多个级别的粒度段，以克服固定大小的方法的问题。我们在CrowdMask中实现了我们的方法，这是一个系统，允许具有潜在敏感内容的图像通过出现在逐渐变大，更可识别的片段中来进行掩蔽，并在识别出风险后立即掩蔽图像的部分。我们对4134名Mechanical Turk工人的实验表明，CrowdMask可以有效地从图像中屏蔽私人内容，而不会向组成工人透露敏感内容，同时仍然使未来的系统能够使用过滤结果。
Crowd-powered systems leverage human intelligence to go beyond the capabilities of automated systems, but also introduce privacy and security concerns because unknown people must view the data that the system processes. While automated approaches cannot robustly filter private information from these datasets, people have the ability to do so if the risk from them viewing the data can be mitigated. We present a crowd-powered approach to masking private content in data by segmenting and distributing smaller segments to crowd workers so that individual workers can identify potentially private content without being able to fully view it themselves. We introduce a novel pyramid workflow for segmentation that uses segments at multiple levels of granularity to overcome problems with fixed-sized approaches. We implement our approach in CrowdMask, a system that allows images with potentially sensitive content to be masked by appearing in progressively larger, more identifiable segments, and masking portions of the image as soon as a risk is identified. Our experiments with 4134 Mechanical Turk workers show that CrowdMask can effectively mask private content from images without revealing sensitive content to constituent workers, while still enabling future systems to use the filtered result.