EAGER: TWC: Collaborative: iPrivacy: Automatic Recommendation of Personalized Privacy Settings for Image Sharing

EAGER：TWC：协作：iPrivacy：自动推荐图像共享的个性化隐私设置

• 批准号: 1852554
• 负责人: Dan Lin
• 金额: $ 10.57万
• 依托单位: University of Missouri-Columbia
• 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: 2018-08-15 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1852554&HistoricalAwards=false
• 关键词: EAGER; TWC; Collaborative; iPrivacy; Automatic

The objective of this project is to investigate a comprehensive image privacy recommendation system, called iPrivacy (image Privacy), which can efficiently and automatically generate proper privacy settings for newly shared photos that also considers consensus of multiple parties appearing in the same photo. Photo sharing has become very popular with the growing ubiquity of smartphones and other mobile devices. However, many people especially young users of social networks often share private photos about themselves and their friends without being aware of the potential impact on their future lives caused by unwanted disclosure and privacy violations. Although some photo sharing platforms start to offering functions of privacy configuration, such manual process could be very tedious for users and also error-prone since not many users have sufficient background knowledge about privacy. This project will address these rising privacy concerns of photo sharing in social sites and benefit billions of social network users. The broader impact of this project will be further enhanced by the integration of education and research. A range of educational activities will be carried out including curriculum development, professional training for students and cybersecurity camp for K-12 teachers, with emphasis to under-represented groups.This project will seamlessly integrate expertise from two different domains: image understanding and privacy management, leading to one of the first comprehensive and automatic policy recommendation systems. The proposed project contains the following innovative researches. First, a multi-party privacy-sensitive object identification algorithm will be developed which will be capable of automatically generating the identity of each human subject in a photo so as to automate the subsequent privacy harmonization process. Second, a unique privacy harmonization approach will be designed, which will conduct hierarchical privacy policy mining to understand different levels of privacy concerns in communities, recommend policies that effectively harmonize privacy preferences of multiple people appearing in the same photo and also adapt to the evolution of people's privacy preferences. The proposed iPrivacy system will not only fully release the burden of privacy configuration at users' side, but will also promote better privacy practice based on knowledge learned from large-scale historical and societal information.

本项目的目标是研究一个综合的图像隐私推荐系统，称为iPrivacy（图像隐私），它可以有效地自动生成适当的隐私设置，为新共享的照片，也考虑了出现在同一张照片中的多方的共识。随着智能手机和其他移动的设备的日益普及，照片共享已经变得非常流行。然而，许多人，尤其是社交网络的年轻用户，经常分享关于他们自己和他们的朋友的私人照片，而没有意识到不必要的披露和隐私侵犯对他们未来生活的潜在影响。虽然一些照片共享平台开始提供隐私配置的功能，但是这种手动过程对于用户来说可能非常繁琐并且还容易出错，因为没有多少用户具有关于隐私的足够背景知识。该项目将解决社交网站中照片共享的隐私问题，并使数十亿社交网络用户受益。教育与研究的结合将进一步加强该项目的广泛影响。该项目将开展一系列教育活动，包括课程开发、学生专业培训和针对K-12教师的网络安全夏令营，重点关注代表性不足的群体。该项目将无缝整合两个不同领域的专业知识：图像理解和隐私管理，从而成为首个全面自动的策略建议系统之一。该项目包括以下创新研究。首先，将开发一种多方隐私敏感对象识别算法，该算法将能够自动生成照片中每个人类主体的身份，以便自动化随后的隐私协调过程。其次，将设计一种独特的隐私协调方法，该方法将进行分层隐私策略挖掘，以了解社区中不同级别的隐私问题，并推荐有效协调同一照片中出现的多个人的隐私偏好的策略，并适应人们隐私偏好的演变。建议的iPrivacy系统不仅将完全释放用户侧的隐私配置负担，而且还将基于从大规模历史和社会信息中学习的知识促进更好的隐私实践。

项目成果

iPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning

• DOI: 10.1109/tifs.2016.2636090
• 发表时间: 2017-05-01
• 期刊: IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
• 影响因子: 6.8
• 作者: Yu, Jun;Zhang, Baopeng;Fan, Jianping
• 通讯作者: Fan, Jianping

RIPA: Real-Time Image Privacy Alert System

RIPA：实时图像隐私警报系统

• DOI: 10.1109/cic.2018.00029
• 发表时间: 2018
• 期刊: 2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC
• 作者: Keerthi Chandra, Dakshak;Chowgule, Weerdhawal;Fu, Yanjie;Lin, Dan
• 通讯作者: Lin, Dan

From Tag to Protect: A Tag-Driven Policy Recommender System for Image Sharing

• DOI: 10.1109/pst.2017.00047
• 发表时间: 2017-08
• 期刊: 2017 15th Annual Conference on Privacy, Security and Trust (PST)
• 作者: A. Squicciarini;Andrea Novelli;D. Lin;Cornelia Caragea;Haoti Zhong

Leveraging Content Sensitiveness and User Trustworthiness to Recommend Fine-Grained Privacy Settings for Social Image Sharing

利用内容敏感性和用户可信度为社交图像共享推荐细粒度的隐私设置

• DOI: 10.1109/tifs.2017.2787986
• 发表时间: 2018-05
• 期刊: IEEE Transactions on Information Forensics and Security
• 影响因子: 6.8
• 作者: Yu Jun;Kuang Zhenzhong;Zhang Baopeng;Zhang Wei;Lin Dan;Fan Jianping
• 通讯作者: Fan Jianping

UFace: Your Universal Password That No One Can See

• DOI: 10.1016/j.cose.2017.09.016
• 发表时间: 2017-10
• 期刊: Comput. Secur.
• 作者: Nicholas Hilbert;C. Jensen;D. Lin;Wei Jiang