Biblio

The use of video surveillance (VS) has grown significantly using the internet as a platform. Thus security issues on such videos must be addressed. Video frames can have multiple objects and various features over video length. Moving object detection (MOD) and real-time tracking requires security strategies designed to protect videos. This paper is proposed to design an asymmetric encryption method (RSA). The paper has contributed in two stages. In the first phase the fast video segmentation method based on a global variable threshold is designed to facilitate MOD. Later in second pass the RSA-based encryption is used to maintain the efficiency of the object detection. The secure key generation method is demonstrated. The performances of two global thresholds are demonstrated and compared under the encrypted video data. It is found that that method is very effective in finding objects under the context of video surveillance in real time.

In era of information age, due to different electronic, information & communication technology devices and process like sensors, cloud, individual archives, social networks, internet activities and enterprise data are growing exponentially. The most challenging issues are how to effectively manage these large and different type of data. Big data is one of the term named for this large and different type of data. Due to its extraordinary scale, privacy and security is one of the critical challenge of big data. At the every stage of managing the big data there are chances that privacy may be disclose. Many techniques have been suggested and implemented for privacy preservation of large data set like anonymization based, encryption based and others but unfortunately due to different characteristic (large volume, high speed, and unstructured data) of big data all these techniques are not fully suitable. In this paper we have deeply analyzed, discussed and suggested how an existing approach "differential privacy" is suitable for big data. Initially we have discussed about differential privacy and later analyze how it is suitable for big data.