Constructing distributed Hippocratic video databases for privacy-preserving online patient training and counseling.
Constructing distributed Hippocratic video databases for privacy-preserving online patient training and counseling.
复制标题
构建分布式希波克拉底视频数据库以保护隐私的在线患者培训和咨询
DOI：
10.1109/titb.2009.2029695
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发表时间：
2010-07
期刊：
影响因子：
--
通讯作者：
Fan J
中科院分区：
文献类型：
--
作者：
Peng J;Babaguchi N;Luo H;Gao Y;Fan J
数字视频现在在支持更有利可图的在线患者培训和咨询方面发挥着重要作用，来自医疗保健网络中多个竞争组织的患者培训视频的整合将为患者提供更好的服务。然而，隐私问题通常会阻止多个竞争组织共享和整合他们的患者培训视频。此外，患有传染病或慢性病的患者可能不希望在线患者培训组织识别他们是谁，甚至他们对哪些视频片段感兴趣。因此，迫切需要开发更有效的技术来保护视频内容隐私和访问隐私。在本文中，我们开发了一种新的方法来构建一个分布式希波克拉底视频数据库系统，以支持更有利可图的在线患者培训和咨询。首先，一个新的数据库建模方法，支持面向概念的视频数据库组织和分配的隐私度的视频内容自动为每个数据库级别。其次，提出了一种新的算法，通过自动滤除隐私敏感的人体对象，在视频片段的层次上保护视频内容的隐私。为了整合来自多个竞争组织的患者训练视频以构建集中式视频数据库索引结构，开发了隐私保护视频共享方案以支持隐私保护分布式分类器训练并防止来自共享用于视频分类器交叉验证的视频的统计推断。我们在大规模视频数据库上的实验也提供了非常令人信服的结果。
Digital video now plays an important role in supporting more profitable online patient training and counseling, and integration of patient training videos from multiple competitive organizations in the health care network will result in better offerings for patients. However, privacy concerns often prevent multiple competitive organizations from sharing and integrating their patient training videos. In addition, patients with infectious or chronic diseases may not want the online patient training organizations to identify who they are or even which video clips they are interested in. Thus, there is an urgent need to develop more effective techniques to protect both video content privacy and access privacy . In this paper, we have developed a new approach to construct a distributed Hippocratic video database system for supporting more profitable online patient training and counseling. First, a new database modeling approach is developed to support concept-oriented video database organization and assign a degree of privacy of the video content for each database level automatically. Second, a new algorithm is developed to protect the video content privacy at the level of individual video clip by filtering out the privacy-sensitive human objects automatically. In order to integrate the patient training videos from multiple competitive organizations for constructing a centralized video database indexing structure, a privacy-preserving video sharing scheme is developed to support privacy-preserving distributed classifier training and prevent the statistical inferences from the videos that are shared for cross-validation of video classifiers. Our experiments on large-scale video databases have also provided very convincing results.