Social Media Platforms (SMPs) have changed how we communicate, share, and obtain information. However, this also comes at a cost, as users (willingly) share their Privately Sensitive Data (PSDs), such as pictures, real-time locations, and other personal connections, on SMPs. Recently, privacy concerns have gained much attention from both academia and industry . The current literature lacks the privacy risk assessment model that can lead the management sectors (e.g., industrial, social, and governmental) to cooperate to mitigate the privacy invasion risks of users’ PSDs in SMPs. Hence, we propose a novel assessment model (hereafter referred to as AFPr-AM), suggesting alternative strategies for reducing privacy invasion risks of users’ PSDs in SMPs based on determinant criteria . First, we explore multiple factors from the literature that affect the privacy invasion risks of users’ PSDs. Then, to prioritize the importance of determinant criteria , we seek sixty experts to participate in our survey and rank these factors. Finally, we apply the fuzzy analytical hierarchy process approach for weighting the criteria based on the experts’ opinions. Moreover, we employ a cooperative game theory-based multi criteria decision making framework to assess the possibilities of players’ interactions (e.g., management sectors), considering the weighted criteria as players’ payoffs. Our extensive experiments demonstrate that the AFPr-AM model provides effective strategic alternatives to mitigate the possible invasion risks of users’ PSDs in SMPs.

User authentication remains a challenging issue, despite the existence of a large number of proposed solutions, such as traditional text-based, graphical-based, biometrics-based, Web-based, and hardware-based schemes. For example, some of these schemes are not suitable for deployment in an Internet of Things (IoT) setting, partly due to the hardware and/or software constraints of IoT devices. The increasing popularity and pervasiveness of IoT equipment in a broad range of settings reinforces the importance of ensuring the security and privacy of IoT devices. Therefore, in this article, we conduct a comprehensive literature review and an empirical study to gain an in-depth understanding of the different authentication schemes as well as their vulnerabilities and deficits against various types of cyberattacks when applied in IoT-based systems. Based on the identified limitations, we recommend several mitigation strategies and discuss the practical implications of our findings.

While encryption can prevent unauthorized access to a secret message , it does not provide undetectability of covert communications over the public network. Implementing a highly latent data exchange, especially with low eavesdropping/discovery probability, is challenging for practical scenarios, such as social and political movements in authoritarian regimes , military operations, and privacy preservation . Moreover, the current literature suffers from a low embedding capacity and monolingual applicability, limiting the amount of hiding secret data within short text messages using state-of-the-art algorithms, e.g., linguistic-based, structural-based, or coverless-based solutions. In this paper, we present a systematic covert communication technique called CovertSYS that enables a multilingual secure end-to-end conversation via messaging or social network platforms. The CovertSYS functions by encrypting a confidential message using a multi-factor authentication scheme and converting the encoded binary data into hidden Unicode symbols to be transmitted under cover of short text messages. We then conduct extensive experiments to confirm the security and validity of the proposed technique against state-of-the-art approaches. Our experimental results show that the CovertSYS provides a superior mean performance of 91.53% by improving the criteria scores: embedding capacity rate of 100%, imperceptibility rate of 76.4%, and distortion robustness rate of 98.2%. Finally, we discuss the practical implications of the proposed technique compared to the existing text steganography methods.