SURVEY ON DEEP LEARNING TRUST BEHAVIORAL METHODS TO IMPROVE THE BIG DATA PRIVACY OF COVID DATASETS - R. Suban, K.Sakthivel, R.Karunia krishnapriya, G.Vinodhini, (Design Engineering, 2021)
In recent times the privacy breaches is occurring very frequently, where data privacy is at a great risk and the firms are at most risk affecting its loyalty amongst the customers. Hence, the secure access or the privacy of data should be achieved with greater agility and security. Meanwhile Community detection plays a major role in monitoring the opinion or trust and the forms personalized recommendation based on the firms. In comparison with existing behavioral trust modelling with the multiple users, the operational big scale data and privacy and linking the relationship between them requires a comprehensive data analytics framework. The relationship between the security and large-scale data is handled in the proposed system, where a behavioral trust based data analytics modelling is utilized to handle the firm’s privacy data. A deep learning model is used to find the trust relationship between them and it is efficient in handling rapid flow of data at a time. In this paper, we hence provide a comprehensive survey on various deep learning models that supports the privacy of covid-19 datasets in terms of trust model. The study aims to offer various insights on methods adopted to improve the privacy of big datasets. Further, the implications of future scope is presented for the readers to improve on their research in the relevant fields.