Encrypted Feature Extraction for Privacy SIFT
M.M.V.S.Lakshmi1, Ravi Mathey2
1M.V.S.Lakshmi Studying .MTech in Vidya Jyothi Institute of Technology, Hyderabad, India.
2Ravi Mathey, Associate Professor and HOD of CSE Department at Vidya Jyothi Institute of Technology (VJIT), Hyderabad, India.
Manuscript received on October 11, 2013. | Revised Manuscript received on October 15, 2013. | Manuscript published on October 25, 2013. | PP:23-26 | Volume-1, Issue-12, October 2013. | Retrieval Number: L05091011213/2013©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Privacy has acquired considerable attention but is still largely ignored inside multimedia local community. Consider some sort of cloud research scenario the spot that the server can be resource-abundant, and is also capable regarding finishing the particular designated duties. It can be envisioned in which secure advertising applications using privacy preservation is going to be treated seriously. In view that the scale-invariant characteristic transform (SIFT) has become widely adopted in several fields, this project may be the first to the fact that privacy-preserving SORT (PPSIFT) and to address the condition of safeguarded SIFT characteristic extraction and also representation inside encrypted domain.
Keywords: Feature Extraction, Privacy Preserving, Security.