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Progressive Scrambling for Social Media

Progressive Scrambling for Social Media

Wei Qi Yan, Xiaotian Wu, Feng Liu
Copyright: © 2018 |Volume: 10 |Issue: 2 |Pages: 18
ISSN: 1941-6210|EISSN: 1941-6229|EISBN13: 9781522543619|DOI: 10.4018/IJDCF.2018040104
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MLA

Yan, Wei Qi, et al. "Progressive Scrambling for Social Media." IJDCF vol.10, no.2 2018: pp.56-73. http://doi.org/10.4018/IJDCF.2018040104

APA

Yan, W. Q., Wu, X., & Liu, F. (2018). Progressive Scrambling for Social Media. International Journal of Digital Crime and Forensics (IJDCF), 10(2), 56-73. http://doi.org/10.4018/IJDCF.2018040104

Chicago

Yan, Wei Qi, Xiaotian Wu, and Feng Liu. "Progressive Scrambling for Social Media," International Journal of Digital Crime and Forensics (IJDCF) 10, no.2: 56-73. http://doi.org/10.4018/IJDCF.2018040104

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Abstract

Despite research work achieving progress in preserving the privacy of user profiles and visual surveillance, correcting problems in social media have not taken a great step. The reason is the lack of effective modelling, computational algorithms, and resultant evaluations in quantitative research. In this article, the authors take social media into consideration and link users together under the umbrella of social networks so as to exploit a way that the potential problems related to media privacy could be solved. The author's contributions are to propose tensor product-based progressive scrambling approaches for privacy preservation of social media and apply our approaches to the given social media which may encapsulate privacy before being viewed so as to achieve the goal of privacy preservation in anonymity, diverse and closeness. These approaches fully preserve the media information of the scrambled image and make sure it is able to be restored. The results show the proposed privacy persevering approaches are effective and have outstanding performance in media privacy preservation.

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