Volume 30, Issue 3, May 2017, Pages 385–388
Shraddha Babasaheb Kote1, M. Ganeshwade Mandakini2, and K. Vengatesan3
1 ME (CSE), MIT college, Aurangabad, India
2 Assistant Professor, Department of Computer Science Engineering, Marathwada Institute of Technology, Aurangabad, India
3 Assistant Professor, Department of Computer Science Engineering, Marathwada Institute of Technology, Aurangabad, India
Original language: English
Copyright © 2017 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Social Network is an emerging E-service for content sharing sites (CSS). It is emerging service which provides a reliable communication, through this communication a new attack ground for data hackers; they can easily misuses the data through these media. Some users over CSS affects users privacy on their personal contents, where some users keep on sending unwanted comments and messages by taking advantage of the users’ inherent trust in their relationship network. By this privacy of the user data may be loss for this issue this paper handles the most prevalent issues and threats targeting different CSS recently. This proposes a privacy policy prediction and access restrictions along with blocking scheme for social sites using data mining techniques. To perform this, the system utilizes APP (Access Policy Prediction) and Access control mechanism by applying BIC algorithm (Bayesian Information Criterion).
Author Keywords: Adaptive Privacy Policy Prediction (A3P), A3P- Core, A3P- Social, Polar Fourier Transform (PFT).
Shraddha Babasaheb Kote1, M. Ganeshwade Mandakini2, and K. Vengatesan3
1 ME (CSE), MIT college, Aurangabad, India
2 Assistant Professor, Department of Computer Science Engineering, Marathwada Institute of Technology, Aurangabad, India
3 Assistant Professor, Department of Computer Science Engineering, Marathwada Institute of Technology, Aurangabad, India
Original language: English
Copyright © 2017 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Social Network is an emerging E-service for content sharing sites (CSS). It is emerging service which provides a reliable communication, through this communication a new attack ground for data hackers; they can easily misuses the data through these media. Some users over CSS affects users privacy on their personal contents, where some users keep on sending unwanted comments and messages by taking advantage of the users’ inherent trust in their relationship network. By this privacy of the user data may be loss for this issue this paper handles the most prevalent issues and threats targeting different CSS recently. This proposes a privacy policy prediction and access restrictions along with blocking scheme for social sites using data mining techniques. To perform this, the system utilizes APP (Access Policy Prediction) and Access control mechanism by applying BIC algorithm (Bayesian Information Criterion).
Author Keywords: Adaptive Privacy Policy Prediction (A3P), A3P- Core, A3P- Social, Polar Fourier Transform (PFT).
How to Cite this Article
Shraddha Babasaheb Kote, M. Ganeshwade Mandakini, and K. Vengatesan, “By Using SIFT Algorithm: Privacy Policy Inference of User-Uploaded Images on Content Sharing Sites,” International Journal of Innovation and Scientific Research, vol. 30, no. 3, pp. 385–388, May 2017.
