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Monthly Archives: August 2016

Computational Models of Trust and Reputation: Agents, Evolutionary Games, and Social Networks

 

Many recent studies of trust and reputation are made in the context of commercial reputation or rating systems for online communities. Most of these systems have been constructed without a formal rating model or much regard for our sociological understanding of these concepts.

This paper “propose[s] a mathematical framework for modeling trust and reputation that is rooted in findings from the social sciences. In particular, our framework makes explicit the importance of social information (i.e., indirect channels of inference) in aiding members of a social network choose whom they want to partner with or to avoid. Rating systems that make use of such indirect channels of inference are necessarily personalized in nature, catering to the individual context of the rater”.

http://groups.csail.mit.edu/medg/ftp/lmui/computational%20models%20of%20trust%20and%20reputation.pdf

Centralized to Decentralized Social Networks—Factors That Matter

This work covers the research work on decentralization of Online Social Networks (OSNs), issues with centralized design are studied with possible decentralized solutions. Centralized architecture is prone to privacy breach, p2p architecture for data and thus authority decentralization with encryption seems a possible solution. OSNs’ users grow exponentially causing scalability issue, a natural solution is decentralization where users bring resources with them via personal machines or paid services. Also centralized services are not available unremittingly, to this end decentralization proposes replication. Decentralized solutions are also proposed for reliability issues arising in centralized systems and the potential threat of a central authority. Yet key to all problems isn’t found, metadata may be enough for inferences about data and network traffic flow can lead to information on users’ relationships. First issue can be mitigated by data padding or splitting in uniform blocks. Caching, dummy traffic or routing through a mix of nodes can be some possible solutions to the second.

Chapter 3 of “Managing and Processing Big Data in Cloud Computing”.

http://www.igi-global.com/book/managing-processing-big-data-cloud/139330