Secrecy of Uploaded Images on Locates Using A3P Algorithm

Sivanantham Sivakumar; Musrathasleema Abdul Rahman; Nandhini Balu; Nivetha Nainar

doi:doi:10.11648/j.mlr.20170203.11

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Secrecy of Uploaded Images on Locates Using A3P Algorithm

Sivanantham Sivakumar, Musrathasleema Abdul Rahman, Nandhini Balu, Nivetha Nainar

Published in Machine Learning Research (Volume 2, Issue 3)

Received: 13 February 2017 Accepted: 15 March 2017 Published: 29 March 2017

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Abstract

With increasing volume of images users share through social sites, maintaining privacy has become a major problem, as demonstrated by a recent wave of publicized incidents where users individually shared personal information. In light of these incidents, the need of tools to help users control access to their shared content is apparent. Toward addressing this need, we propose an Adaptive Privacy Policy Prediction (A3P) system to help users compose privacy settings for their images. We examine the role of social context, image content and metadata as possible indicators of user’s privacy preferences. We propose a two-level framework which according to the user’s available history on the site, determines the best available privacy policy for the user’s images being uploaded. Our solution relies on an image classification framework for images categories which may be associated with similar policies on a policy prediction algorithm to automatically generate a policy for each newly uploaded image and also according to user’s social features. Over time, generated policies will follow the evolution of user’s privacy attitude.

Published in	Machine Learning Research (Volume 2, Issue 3)
DOI	10.11648/j.mlr.20170203.11
Page(s)	78-85
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Content Sharing Sites, Privacy, Metadata, A3P, Social Media

References

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[9]	A. Mazzia, K. LeFevre, and A. E., “The PViz comprehension tool for social network privacy settings,” in Proc. Symp. Usable Privacy Security, 2012.
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[12]	C. A. Yeung, L. Kagal, N. Gibbins, and N. Shadbolt, “Providing access control to online photo albums based on tags and linked data,” in Proc. Soc. Semantic Web: Where Web 2.0 Meets Web 3.0 at the AAAI Symp., 2009, pp. 9–14.
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APA Style

Sivanantham Sivakumar, Musrathasleema Abdul Rahman, Nandhini Balu, Nivetha Nainar. (2017). Secrecy of Uploaded Images on Locates Using A3P Algorithm. Machine Learning Research, 2(3), 78-85. https://doi.org/10.11648/j.mlr.20170203.11

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ACS Style

Sivanantham Sivakumar; Musrathasleema Abdul Rahman; Nandhini Balu; Nivetha Nainar. Secrecy of Uploaded Images on Locates Using A3P Algorithm. Mach. Learn. Res. 2017, 2(3), 78-85. doi: 10.11648/j.mlr.20170203.11

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AMA Style

Sivanantham Sivakumar, Musrathasleema Abdul Rahman, Nandhini Balu, Nivetha Nainar. Secrecy of Uploaded Images on Locates Using A3P Algorithm. Mach Learn Res. 2017;2(3):78-85. doi: 10.11648/j.mlr.20170203.11

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@article{10.11648/j.mlr.20170203.11,
  author = {Sivanantham Sivakumar and Musrathasleema Abdul Rahman and Nandhini Balu and Nivetha Nainar},
  title = {Secrecy of Uploaded Images on Locates Using A3P Algorithm},
  journal = {Machine Learning Research},
  volume = {2},
  number = {3},
  pages = {78-85},
  doi = {10.11648/j.mlr.20170203.11},
  url = {https://doi.org/10.11648/j.mlr.20170203.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20170203.11},
  abstract = {With increasing volume of images users share through social sites, maintaining privacy has become a major problem, as demonstrated by a recent wave of publicized incidents where users individually shared personal information. In light of these incidents, the need of tools to help users control access to their shared content is apparent. Toward addressing this need, we propose an Adaptive Privacy Policy Prediction (A3P) system to help users compose privacy settings for their images. We examine the role of social context, image content and metadata as possible indicators of user’s privacy preferences. We propose a two-level framework which according to the user’s available history on the site, determines the best available privacy policy for the user’s images being uploaded. Our solution relies on an image classification framework for images categories which may be associated with similar policies on a policy prediction algorithm to automatically generate a policy for each newly uploaded image and also according to user’s social features. Over time, generated policies will follow the evolution of user’s privacy attitude.},
 year = {2017}
}

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TY  - JOUR
T1  - Secrecy of Uploaded Images on Locates Using A3P Algorithm
AU  - Sivanantham Sivakumar
AU  - Musrathasleema Abdul Rahman
AU  - Nandhini Balu
AU  - Nivetha Nainar
Y1  - 2017/03/29
PY  - 2017
N1  - https://doi.org/10.11648/j.mlr.20170203.11
DO  - 10.11648/j.mlr.20170203.11
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 78
EP  - 85
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20170203.11
AB  - With increasing volume of images users share through social sites, maintaining privacy has become a major problem, as demonstrated by a recent wave of publicized incidents where users individually shared personal information. In light of these incidents, the need of tools to help users control access to their shared content is apparent. Toward addressing this need, we propose an Adaptive Privacy Policy Prediction (A3P) system to help users compose privacy settings for their images. We examine the role of social context, image content and metadata as possible indicators of user’s privacy preferences. We propose a two-level framework which according to the user’s available history on the site, determines the best available privacy policy for the user’s images being uploaded. Our solution relies on an image classification framework for images categories which may be associated with similar policies on a policy prediction algorithm to automatically generate a policy for each newly uploaded image and also according to user’s social features. Over time, generated policies will follow the evolution of user’s privacy attitude.
VL  - 2
IS  - 3
ER  -

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Author Information

Sivanantham Sivakumar

Department of Information Technology, Adhiyamaan College of Engineering, Hosur, Tamil Nadu, India
Musrathasleema Abdul Rahman

Department of Information Technology, Adhiyamaan College of Engineering, Hosur, Tamil Nadu, India
Nandhini Balu

Department of Information Technology, Adhiyamaan College of Engineering, Hosur, Tamil Nadu, India
Nivetha Nainar

Department of Information Technology, Adhiyamaan College of Engineering, Hosur, Tamil Nadu, India

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Plain Text BibTeX RIS

APA Style

Sivanantham Sivakumar, Musrathasleema Abdul Rahman, Nandhini Balu, Nivetha Nainar. (2017). Secrecy of Uploaded Images on Locates Using A3P Algorithm. Machine Learning Research, 2(3), 78-85. https://doi.org/10.11648/j.mlr.20170203.11

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ACS Style

Sivanantham Sivakumar; Musrathasleema Abdul Rahman; Nandhini Balu; Nivetha Nainar. Secrecy of Uploaded Images on Locates Using A3P Algorithm. Mach. Learn. Res. 2017, 2(3), 78-85. doi: 10.11648/j.mlr.20170203.11

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AMA Style

Sivanantham Sivakumar, Musrathasleema Abdul Rahman, Nandhini Balu, Nivetha Nainar. Secrecy of Uploaded Images on Locates Using A3P Algorithm. Mach Learn Res. 2017;2(3):78-85. doi: 10.11648/j.mlr.20170203.11

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@article{10.11648/j.mlr.20170203.11,
  author = {Sivanantham Sivakumar and Musrathasleema Abdul Rahman and Nandhini Balu and Nivetha Nainar},
  title = {Secrecy of Uploaded Images on Locates Using A3P Algorithm},
  journal = {Machine Learning Research},
  volume = {2},
  number = {3},
  pages = {78-85},
  doi = {10.11648/j.mlr.20170203.11},
  url = {https://doi.org/10.11648/j.mlr.20170203.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20170203.11},
  abstract = {With increasing volume of images users share through social sites, maintaining privacy has become a major problem, as demonstrated by a recent wave of publicized incidents where users individually shared personal information. In light of these incidents, the need of tools to help users control access to their shared content is apparent. Toward addressing this need, we propose an Adaptive Privacy Policy Prediction (A3P) system to help users compose privacy settings for their images. We examine the role of social context, image content and metadata as possible indicators of user’s privacy preferences. We propose a two-level framework which according to the user’s available history on the site, determines the best available privacy policy for the user’s images being uploaded. Our solution relies on an image classification framework for images categories which may be associated with similar policies on a policy prediction algorithm to automatically generate a policy for each newly uploaded image and also according to user’s social features. Over time, generated policies will follow the evolution of user’s privacy attitude.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Secrecy of Uploaded Images on Locates Using A3P Algorithm
AU  - Sivanantham Sivakumar
AU  - Musrathasleema Abdul Rahman
AU  - Nandhini Balu
AU  - Nivetha Nainar
Y1  - 2017/03/29
PY  - 2017
N1  - https://doi.org/10.11648/j.mlr.20170203.11
DO  - 10.11648/j.mlr.20170203.11
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 78
EP  - 85
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20170203.11
AB  - With increasing volume of images users share through social sites, maintaining privacy has become a major problem, as demonstrated by a recent wave of publicized incidents where users individually shared personal information. In light of these incidents, the need of tools to help users control access to their shared content is apparent. Toward addressing this need, we propose an Adaptive Privacy Policy Prediction (A3P) system to help users compose privacy settings for their images. We examine the role of social context, image content and metadata as possible indicators of user’s privacy preferences. We propose a two-level framework which according to the user’s available history on the site, determines the best available privacy policy for the user’s images being uploaded. Our solution relies on an image classification framework for images categories which may be associated with similar policies on a policy prediction algorithm to automatically generate a policy for each newly uploaded image and also according to user’s social features. Over time, generated policies will follow the evolution of user’s privacy attitude.
VL  - 2
IS  - 3
ER  -

Copy | Download