Learning and Preserving Relationship Privacy in Photo Sharing
Learning and Preserving Relationship Privacy in Photo Sharing
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DOI：
10.1109/bdcat56447.2022.00029
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Jialin Liu;Lin Li;Na Li
中科院分区：
文献类型：
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作者：
Jialin Liu;Lin Li;Na Li
近年来，在线社交网络（OSN）已经成为流行的内容共享环境。随着配备高品质摄像头的智能手机的出现，人们喜欢在OSN上分享他们生活瞬间的照片。然而，照片通常包含人们不打算与他人共享的私人信息（例如，敏感的关系）。仅仅依靠OSN用户手动处理照片来保护他们的关系可能会很繁琐，而且容易出错。因此，我们设计了一个系统，自动发现敏感的关系，在一张照片上分享在线和保护的关系，面对封锁技术。我们首先使用决策树模型从OSN用户标记为私人或公共的照片中学习敏感关系。然后，我们定义了一个人脸阻塞问题，并开发了一个线性规划模型，以优化保护关系隐私和维护照片效用之间的权衡。在本文中，我们生成了合成数据，并使用它来评估我们的系统在隐私保护和照片效用损失方面的性能。
In recent years, Online Social Networks (OSN) have become popular content-sharing environments. With the emergence of smartphones with high-quality cameras, people like to share photos of their life moments on OSNs. The photos, however, often contain private information that people do not intend to share with others (e.g., their sensitive relationship). Solely relying on OSN users to manually process photos to protect their relationship can be tedious and error-prone. Therefore, we designed a system to automatically discover sensitive relations in a photo to be shared online and preserve the relations by face blocking techniques. We first used the Decision Tree model to learn sensitive relations from the photos labeled private or public by OSN users. Then we defined a face blocking problem and developed a linear programming model to optimize the tradeoff between preserving relationship privacy and maintaining the photo utility. In this paper, we generated synthetic data and used it to evaluate our system performance in terms of privacy protection and photo utility loss.