Anjanadevi B,Nagesh Vadaparthi, Jyothi V,Satyanarayana Reddy G/ International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.560-566 560 | P a g e A Novel Approach For Privacy Preserving Videosharing And Merging Anjanadevi B,Nagesh Vadaparthi, Jyothi V,Satyanarayana Reddy G Department of IT, MVGR College of Engineering, Vizianagaram, AP, India Abstract In present days rapid growth of internet has paved a path for increased utilization of distributed applications. The number of applications aredrastically increased for a distribution of video information to various places. In recent years, videos are also playingmajor role at various surveillance applications. Hence, propose a novel approach to share the videos to various places while providing privacy.In this paper we used an efficient algorithm to merge the given video. This method provides various parameters to preserve the privacy and accuracy. Our proposed framework is highly efficient than various existing approaches like Smart Cameras, Homomorphic Encryption and Secure Multi-Party computation to carry out privacy preserving video surveillance. This work opens up a new avenue for practical and provably secure implementations of vision algorithms. The proposed system along with motion segmentation results will be used to detect and track peoples or objects. Index Terms-Privacy, Video Surveillance, Sharing algorithm, Merging Algorithm, Vision algorithms I. INTRODUCTION The Growth of internet has increased the usage and distribution of multimedia content among remote locations.In present internet form, lack of security is observed while distributing the multimedia information.But security of sensitive information [1] is of primary concern in the field of commercial,medical, military systems and even at work places.Privacy plays a major role in internet applications. Privacy is pertains to data is "freedom from unauthorized intrusion". With respect to privacy-preserving data mining [2]. If users have given the authorization to use data for the particular data mining task, then there is no privacy issue. And also the user is not authorized, what user constitutes ―intrusion‖ A common standard among most privacy laws (Ex-European Community privacy guidelines or the U.S. healthcare laws) is that privacy only applies to ―individually identifiable data‖. By combining intrusion and individually identifiable leads to a standard to judge privacy preserving data mining. A privacy-preserving data mining technique must ensure that any information which is disclosed ―cannot be traced to an individual‖ or ―does not constitute an intrusion ―. An improvement in our knowledge about an individual could be considered an intrusion. The latter is particularly likely to cause a problem for data mining, as the goal is to improve our knowledge. Even though the target is often groups of individuals, knowing more about a group does increase our knowledge about individuals in the group. This means we need to measure both the knowledge gained and our ability to relate it to a particular individual, and determine if these exceed the thresholds.Privacy, therefore, happens to be serious concern in the age of video surveillance. Widespread usage of surveillance cameras [3], in offices and other business establishments, pose a significant threat to the privacy of the employees and visitors. It raises the specter of an invasive `Big Brother' society. In this regards, certain privacy laws have been introduced to guard an individual‗s privacy/rights. Despite these, video surveillance remains vulnerable to abuse by unscrupulous operators with criminal or voyeuristic aims and to institutional abuse for incriminatory purposes. These legitimate concerns frequently slow the deployment of surveillance systems. The challenge of introducing privacy and security in such a practical surveillance system has been stifled by enormous computational and communication overhead required by the solutions. The privacy of the system is based on splitting the information present in an image into multiple shares. The parameters used for shattering are primes p 1, p 2 ,p 3 ……p i and scale factor ‗sc‘ are constant for each shattering operation and are in general assumed to be public. The only possible information leakage of the secret is that retained by each share. We now analytically show that with an optimal parameter selection, the information retained by a share is negligible.Information privacy is concerned with preserving the confidentiality of information and is therefore the most relevant kind of privacy with respect to the internet and email monitoring or electronic monitoring [3]. Therefore, it is essential to develop an efficient method which can ensure that the data is not tampered. Though encryption techniques are popular and assures the