REMIND: Risk Estimation Mechanism for Images in Network Distribution
REMIND: Risk Estimation Mechanism for Images in Network Distribution
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DOI：
10.1109/tifs.2019.2924853
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发表时间：
2020
影响因子：
6.8
通讯作者：
D. Lin;Douglas Steiert;Joshua Morris;A. Squicciarini;Jianping Fan
中科院分区：
文献类型：
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作者：
D. Lin;Douglas Steiert;Joshua Morris;A. Squicciarini;Jianping Fan
人们经常通过各种社交媒体网站与他人分享他们的照片。借助社交媒体网站提供的隐私设置，图像所有者可以指定共享范围，例如，密友和熟人。然而，即使照片的所有者小心地设置隐私设置以排除不应该看到照片的给定个人，照片仍然可能最终到达更广泛的观众，包括那些通过意外的披露渠道明显不受欢迎的观众，从而导致隐私泄露。此外，通常的情况是，给定的图像涉及多个利益相关者，他们也在照片中被描绘。由于不同的个性，就这些多所有者照片的隐私设置达成一致更具挑战性。在本文中，我们提出了一个隐私风险提醒系统，称为提醒，它估计的概率，共享的照片可能会被不需要的人看到-通过社交图-谁不包括在原来的共享列表。我们从一个新的角度来解决这个问题，通过挖掘关于图像共享历史的大数据。具体地，社交媒体提供商拥有大量的图像共享信息（例如，哪些照片与谁共享）。通过分析和建模这样丰富的信息，我们建立了一个复杂的概率模型，有效地聚合图像泄漏概率沿着不同的可能的图像传播链和循环。如果计算的公开概率指示隐私泄露的高风险，则向图像所有者发出提醒以帮助修改隐私设置（或者至少通知用户关于该意外公开风险）。建议的REMIND系统也有一个很好的特点，政策协调，有助于解决多个所有者的照片隐私差异。我们已经进行了用户研究，以验证我们提出的解决方案的基本原理，并进行了实验研究，以评估拟议的REMIND系统的效率。
People constantly share their photographs with others through various social media sites. With the aid of the privacy settings provided by social media sites, image owners can designate scope of sharing, e.g., close friends and acquaintances. However, even if the owner of a photograph carefully sets the privacy setting to exclude a given individual who is not supposed to see the photograph, the photograph may still eventually reach a wider audience, including those clearly undesired through unanticipated channels of disclosure, causing a privacy breach. Moreover, it is often the case that a given image involves multiple stakeholders who are also depicted in the photograph. Due to various personalities, it is even more challenging to reach agreement on the privacy settings for these multi-owner photographs. In this paper, we propose a privacy risk reminder system, called REMIND, which estimates the probability that a shared photograph may be seen by unwanted people—through the social graph—who are not included in the original sharing list. We tackle this problem from a novel angle by digging into the big data regarding image sharing history. Specifically, the social media providers possess a huge amount of image sharing information (e.g., what photographs are shared with whom) of their users. By analyzing and modeling such rich information, we build a sophisticated probability model that efficiently aggregates the image disclosure probabilities along different possible image propagation chains and loops. If the computed disclosure probability indicates high risks of privacy breach, a reminder is issued to the image owner to help revise the privacy settings (or, at least, inform the user about this accidental disclosure risk). The proposed REMIND system also has a nice feature of policy harmonization that helps resolve privacy differences in multi-owner photographs. We have carried out a user study to validate the rationale of our proposed solutions and also conducted experimental studies to evaluate the efficiency of the proposed REMIND system.