Online social media platforms provide a range of benefits, such as conversation and information sharing, as well as marketing and advertising for businesses. However, these platforms are soft targets for bad actors to disseminate misinformation or rumors. Untrustworthy content on social media poses a great threat to truth since any user can produce unverified online content to gain popularity. It has been realized that fake information and accounts create a great deal of confusion. To determine user credibility and promote reliable information, we propose UCreDiSSiT method, which incorporates a user's domain of interest, social relations, and temporal features. The suggested approach draws inspiration from earlier works but differs in weighing factors, formalizing factors, and addressing extreme circumstances in large-scale deployment. The experiments are conducted on real-time users' data on Twitter. Our results demonstrate the effectiveness of the proposed method.

Cyber attacks on data, networks, and software have become a crucial problem for supply chain management due to the globalization, decentralization, and digitalization. Blockchain provides an ideal platform for business stakeholders to address issues with modern supply chains, such as traceability, interoperability, and transparency. However, adopting blockchain is challenging as it introduces risks to the supply chain.In this paper, we propose a blockchain-based framework to manage the supply chain and enable a trust-based feedback mechanism, fostering trust among supply chain stakeholders. Moreover, we perform a qualitative risk assessment for adopting blockchain in the supply chain management process, based on standards provided by the National Institute of Standards and Technology (NIST). Our assessment shows that if a threat is imminent, the risk associated with the consensus, limited fixed verification capacity, and inter-autonomous system communication is high in a blockchain-based supply chain that uses proof of authority.

With the increasing adoption of digital health platforms through mobile apps and online services, people have greater flexibility connecting with medical practitioners, pharmacists, and laboratories and accessing resources to manage their own health-related concerns. Many healthcare institutions are connecting with each other to facilitate the exchange of healthcare data, with the goal of effective healthcare data management. The contents generated over these platforms are often shared with third parties for a variety of purposes. However, sharing healthcare data comes with the potential risk of exposing patients’ sensitive information to privacy threats. In this article, we address the challenge of sharing healthcare data while protecting patients’ privacy. We first model a complex healthcare dataset using a heterogeneous information network that consists of multi-type entities and their relationships. We then propose DiffHetNet , an edge-based differentially private algorithm, to protect the sensitive links of patients from inbound and outbound attacks in the heterogeneous health network. We evaluate the performance of our proposed method in terms of information utility and efficiency on different types of real-life datasets that can be modeled as networks. Experimental results suggest that DiffHetNet generally yields less information loss and is significantly more efficient in terms of runtime in comparison with existing network anonymization methods. Furthermore, DiffHetNet is scalable to large network datasets.