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

Yu, Jun; Zhang, Baopeng; Kuang, Zhengzhong; Lin, Dan; Fan, Jianping

doi:10.1109/tifs.2016.2636090

Cited by 301 publications

(128 citation statements)

References 36 publications

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Section: Usability and Transparencymentioning

confidence: 99%

“…In particular, a recent trend leverages deep learning techniques for image privacy prediction and privacy-aware image classification [Tonge and Caragea 2016;Yu et al 2017]. For instance, Yu et al [2017] use deep multi-task learning to automatically detect privacy-sensitive objects in images and recommend privacy settings for their protection. A number of approaches (e.g., [Zerr et al 2012;Squicciarini et al 2015;Squicciarini et al 2017]) leverage both visual features of images and other information for privacy-aware image classification and the generation of privacy settings.…”

Section: Usability and Transparencymentioning

confidence: 99%

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Survey on Access Control for Community-Centered Collaborative Systems

2018

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The last decades have seen a growing interest and demand for community-centered collaborative systems and platforms. These systems and platforms aim to provide an environment in which users can collaboratively create, share and manage resources. While offering attractive opportunities for online collaboration and information sharing, they also open several security and privacy issues. This has attracted several research efforts towards the design and implementation of novel access control solutions that can handle the complexity introduced by collaboration. Despite these efforts, transition to practice has been hindered by the lack of maturity of the proposed solutions. The access control mechanisms typically adopted by commercial collaborative systems like online social network websites and collaborative editing platforms, are still rather rudimentary and do not provide users with a sufficient control over their resources. This survey examines the growing literature on access control for collaborative systems centered on communities, and identifies the main challenges to be addressed in order to facilitate the adoption of collaborative access control solutions in real-life settings. Based on the literature study, we delineate a roadmap for future research in the area of access control for community-centered collaborative systems.

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Section: Usability and Transparencymentioning

confidence: 99%

Section: Usability and Transparencymentioning

confidence: 99%

Survey on Access Control for Community-Centered Collaborative Systems

2018

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“…Yu et al [15] present iPrivacy, an approach that automatically identifies privacy-sensitive elements in a photo and blurs them to protect user's privacy. Similarly, Ilia et al [7] suggest blurring the faces of the users depicted in a picture based on each user's preference.…”

Section: Incremental Improvementmentioning

confidence: 99%

SoSharP: Recommending Sharing Policies in Multiuser Privacy Scenarios

Fogues

Murukannaiah

Such

et al. 2017

IEEE Internet Comput.

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“…There are several other research papers which do not fall in the scope of the deep learning model related privacy topic. For example, Yu et al developed an approach to automatically identify privacy sensitive object classes and their privacy settings. Liu et al proposed an algorithm of elaborating adversarial examples to resist the automatic detection system based on the Faster RCNN framework.…”

Section: Introductionmentioning

confidence: 99%

Adversaries or allies? Privacy and deep learning in big data era

Liu

Ding

Zhu

et al. 2018

Concurrency and Computation

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Summary Deep learning methods have become the basis of new AI‐based services on the Internet in big data era because of their unprecedented accuracy. Meanwhile, it raises obvious privacy issues. The deep learning–assisted privacy attack can extract sensitive personal information not only from the text but also from unstructured data such as images and videos. In this paper, we proposed a framework to protect image privacy against deep learning tools, along with two new metrics that measure image privacy. Moreover, we propose two different image privacy protection schemes based on the two metrics, utilizing the adversarial example idea. The performance of our solution is validated by simulations on two different datasets. Our research shows that we can protect the image privacy by adding a small amount of noise that has a humanly imperceptible impact on the image quality, especially for images of complex structures and textures.

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

Cited by 301 publications

References 36 publications

Survey on Access Control for Community-Centered Collaborative Systems

Survey on Access Control for Community-Centered Collaborative Systems

SoSharP: Recommending Sharing Policies in Multiuser Privacy Scenarios

Adversaries or allies? Privacy and deep learning in big data era

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