DTRM: A new reputation mechanism to enhance data trustworthiness for high-performance cloud computing

Abstract

Cloud computing and the mobile Internet have been the two most influential information technology revolutions, which intersect in mobile cloud computing (MCC). The burgeoning MCC enables the large-scale collection and processing of big data, which demand trusted, authentic, and accurate data to ensure an important but often overlooked aspect of big data --data veracity. Troublesome internal attacks launched by internal malicious users is one key problem that reduces data veracity and remains difficult to handle. To enhance data veracity and thus improve the performance of big data computing in MCC, this paper proposes a Data Trustworthiness enhanced Reputation Mechanism (DTRM) which can be used to defend against internal attacks. In the DTRM, the sensitivity-level based data category, Metagraph theory based user group division, and reputation transferring methods are integrated into the reputation query and evaluation process. The extensive simulation results based on real datasets show that the DTRM outperforms existing classic reputation mechanisms under bad mouthing attacks and mobile attacks.

A trust management model based on mutual trust and a reward‐with‐punishment mechanism for cloud environments

“…Li et al 20 proposed a trust‐based service composition agent learning model, which designs a new comprehensive trust management model by using a fuzzy comprehensive evaluation method. Lin et al 21 proposed a data‐credibility‐enhancing mechanism for the prevention of internal attacks, which integrates sensitivity‐based data categories, metagraph‐based user group partitioning, and reputation transfer methods into reputation query and evaluation processes. In addition to the above mentioned, scholars have done research on trust or reputation of social networks 22,23 or web service environments 24,25 …”