Segregation Approach for User Sending Data on Content Sharing Sites

Abstract

: Social Network is an emerging E-service for content sharing sites (CSS). It is efficient service which users communication through this communication a 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. The need of tools to help users control access to their shared data is separate. 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 measures for their data. The role of images and its metadata are changed as a measure of user security models. The Framework determines the best security approach for the uploaded data. To provide security for the data, automated annotation of images is introduced to create the meta data information about the images by using the new approach is called Semantic annotated Markova Semantic Indexing (SMSI) for retrieving 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.. The collations data result in unexpected exposure of one’s social locations and lead to abuse of one’s personal information. We further propose different functions to uses in system recommended functions and provide a security models. For user-specified functions we change secret small functions in which security is enhanced by hiding secret functions/algorithms.