Encrypted Feature Extraction for Privacy SIFT

Provided by: International Journal of Emerging Science and Engineering (IJESE)
Topic: Security
Format: PDF
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 Function 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.

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