With the explosion of multimedia data, machine learning applications are proliferating. Privacy concerns have recently resurfaced, due to a few prominent incidents of user data leakage, which divulged sensitive information to other people. Multimedia data platform providers––social networks, their affiliates or governments with access to users’ content––use algorithms to profile users by extracting or inferring demographic information, personality traits, relationships, opinions, and beliefs. These results, in turn, feed algorithms for targeted advertising but also for personalized content recommendation to maximize user engagement. While this is ostensibly for users’ benefit, they have serious privacy implications. We highlight this new problem of privacy protection against machines in contrast to the traditional problem of privacy protection against humans. We briefly touch upon our initial solution, a human-sensitivity-aware image perturbation model, which is able to modify the computational classification results of sensitive attributes while preserving the remaining attributes. We then point to many exciting open problems in this new area.

中文翻译：

---

针对机器的多媒体数据隐私

随着多媒体数据的爆炸式增长，机器学习应用正在激增。由于一些突出的用户数据泄漏事件（最近将敏感信息泄露给其他人），隐私问题最近再次浮出水面。多媒体数据平台提供商-可以访问用户内容的社交网络，其分支机构或政府-通过提取或推断人口统计信息，个性特征，关系，观点和信念，使用算法来对用户进行配置。这些结果反过来为目标广告提供了算法，也为个性化内容推荐提供了算法，以最大限度地提高用户参与度。虽然表面上这是为了用户的利益，但它们对隐私有严重的影响。与传统的针对人类的隐私保护问题相比，我们着重介绍了针对机器的隐私保护这一新问题。我们简要地谈谈我们的初始解决方案，即一种人类敏感度感知图像扰动模型，该模型能够修改敏感属性的计算分类结果，同时保留其余属性。然后，我们指出这个新领域中许多令人兴奋的开放问题。