This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.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.This work was supported by the National Natural Science Foundation of China (61602360, 61772008, 61472121), the Pilot Project of Fujian Province (formal industry key project) (2016Y0031), the Foundation of Science and Technology on Information Assurance Laboratory (KJ-14-109) and the Fujian Provincial Key Lab of Network Security and Cryptology Research Fund (15012)
