Security Schedule Speculation of User-Uploaded Data in Content Sharing Sites

Krosuri Madhuri, K S M V Kumar


Social Network is efficient security and service for content sharing sites (CSS). It is efficient service users communication new attack ground for data hackers they can easily unused the data in the media. Some users over CSS affect user’s security on their personal data. An Adaptive Privacy Policy Prediction (A3P) model helps users to compose security model for their data. We put forward this model consisting Adaptive Privacy Policy Prediction (A3P) framework to help users create security model for their data. To provide security for the data automated annotation of images is create the meta data information about the images by using the new model is called Semantic annotated Markova Semantic Indexing (SMSI) for take the data The proposed model automatically annotates the data using hidden Markov model and features extracted is using color histogram and scale invariant feature transform data sharing. We further propose many functions to uses in system recommended functions and provide security models. For user-specified functions We change secret small functions in which security is modify by hiding secret functions/algorithms.

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