CRII: SaTC: Image Publication with Differential Privacy

CRII：SaTC：具有差异隐私的图像发布

• 批准号: 1755884
• 负责人: Liyue Fan
• 金额: $ 17.5万
• 依托单位: SUNY at Albany
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1755884&HistoricalAwards=false
• 关键词: CRII; SaTC; Image; Publication; Differential

The publication of image data captured by ubiquitous surveillance devices, such as traffic cameras and security surveillance cameras, would greatly benefit various communities and enable many applications. However, sharing image data with untrusted parties would raise privacy concern due to potential sensitive content, like identities and activities that may be in the images. Standard image obfuscation techniques, such as pixelation and blurring, do not provide effective privacy preservation for people or objects represented in the data. The goal of this project is to quantitatively define the notion of privacy in image data and develop image publication solutions to achieve rigorous privacy guarantees. By formally modeling image privacy, this project promises significant impact in enabling image data sharing with a wide range of recipients while ensuring individual privacy. This project develops rigorous privacy notions based on differential privacy for image data, while accounting for the representation of sensitive content in images and effective image obfuscation algorithms to guarantee privacy, and incorporates widely adopted feature extraction techniques in computer vision. The investigator will evaluate the utility and efficiency of the obfuscation algorithms, including the feasibility for popular image processing applications such as crowd counting and object recognition. The project also includes educational activities for K-12 students and involvement of women and minorities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

由无处不在的监控设备（诸如交通摄像机和安全监控摄像机）捕获的图像数据的发布将极大地有益于各种社区并使许多应用成为可能。然而，与不受信任的方共享图像数据会由于潜在的敏感内容（如图像中可能存在的身份和活动）而引起隐私问题。 标准的图像模糊技术，如像素化和模糊，不能为数据中表示的人或对象提供有效的隐私保护。 该项目的目标是定量定义图像数据中的隐私概念，并开发图像发布解决方案，以实现严格的隐私保证。 通过对图像隐私进行正式建模，该项目有望在确保个人隐私的同时实现与广泛的接收者共享图像数据方面产生重大影响。该项目基于图像数据的差分隐私开发了严格的隐私概念，同时考虑了图像中敏感内容的表示和有效的图像模糊算法以保证隐私，并结合了计算机视觉中广泛采用的特征提取技术。研究人员将评估混淆算法的实用性和效率，包括流行的图像处理应用程序的可行性，如人群计数和对象识别。该项目还包括K-12学生的教育活动以及妇女和少数民族的参与。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。