Achieving secure big data collection based on trust evaluation and true data discovery

Data collection is an important process in the life cycle of big data processing. It is the key part that must be completed first in all kinds of data applications, which determines the results of data analysis and application service quality. However, untrusted data sources and transmission links expose the data collection process to attacks and malicious threats such as counterfeiting, replay, and denial of service, and ultimately lead to untrustworthy data. In order to cope with the threat of data collection process and ensure data quality, this paper proposes trust evaluation scheme for data security collection based on wireless sensor network, one of the data collection applications, including direct trust, recommendation trust, link trust, and backhaul trust. Meanwhile, in order to realize the dynamic update of the trust of the data sources, a true data discovery and trust dynamic update mechanism based on (omega-FCM (Weight Fuzzy C-Mean) algorithm is proposed. The results of a large number of simulation experiments show that the proposed scheme, model and algorithm can effectively evaluate the trust of data sources and ensure the authenticity of the collected data. (C) 2020 Elsevier Ltd. All rights reserved.