Paper Title :Mysterious Profile Matching With Adaptive Transportable Video Streaming And Competent Social Video Sharing In The Clouds

Author :R S Dinesh, S.Komala

Article Citation :R S Dinesh ,S.Komala , (2014 ) " Mysterious Profile Matching With Adaptive Transportable Video Streaming And Competent Social Video Sharing In The Clouds " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 112-121, Volume-2,Issue-9

Abstract : In this paper, we study user profile matching with privacy-preservation in mobile social networks (MSNs) and begin a family of novel profile matching protocols. We first propose an explicit Comparison-based Profile Matching protocol (eCPM) which runs between two parties, an initiator and a responder. The eCPM enable the initiator to obtain the comparison-based matching result about a specified attribute in their profiles, while stop their attribute values from revelation. We then propose an implicit Comparison-based Profile Matching protocol (iCPM) which allows the initiator to straight obtain some messages in its place of the comparison result from the responder. The messages distinct to user profile can be divided into numerous categories by the responder. The initiator unquestioningly selects the engaged classification which is unidentified to the -responder. Two information in each team are ready by the -responder, and only one concept can be acquired by the initiator according to the comparison outcome on only one feature. We further simplify the iCPM to an implicit Predicate-based Profile matching protocol (iPPM) which allows complex comparison criteria spanning multiple attributes. we propose a new mobile video streaming framework, dubbed AMES-Cloud, which has two main parts: AMoV (adaptive mobile video streaming) and ESoV (efficient social video sharing). AMoV and ESoV construct a private agent to provide video streaming services efficiently for each mobile user. For a given user, AMoV lets her secret agent adaptively adjust her stream flow with a scalable video coding technique based on the feedback of link quality. Likewise, ESoV monitors the social network interactions among mobile users, and their private agents try to prefetch video content in advance. We implement a prototype of the AMES-Cloud structure to show its performance. It is shown that the private agents in the clouds can effectively provide the adaptive streaming, and perform video sharing (i.e., prefetching) based on the social network analysis.

Type : Research paper

Published : Volume-2,Issue-9

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