Utsav Upadhyay’s research while affiliated with Sir Padampat Singhania University and other places

In the rapidly evolving digital world, ensuring secure data transmission, especially in image data pivotal to modern communication, remains critical. The Internet of Things (IoT) amplifies the need for encryption systems that blend robust security with efficiency. However, current cryptosystems often compromise one for the other. To address this, emerging algorithmic techniques aim to strike a balance. Among these, First Order Cellular Automata (FOCA) show promise for image encryption, aligning seamlessly with image processing intricacies while maintaining lower implementation complexity. This paper introduces SORCHIC, a novel hybrid encryption algorithm designed specifically for IoT applications encrypting multichannel images. Leveraging Second-Order Cellular Automata (SOCA) and Chaotic Maps, SORCHIC undergoes comprehensive exploration within this work. Detailed insights into its inner workings are provided, followed by a rigorous experimental analysis comparing it to existing encryption methods. The results underscore SORCHIC’s resilience against diverse cryptanalysis attacks and highlight its superior efficiency compared to established techniques. This study contributes to advancing encryption methods, particularly in IoT, by presenting a robust, efficient, and secure approach tailored for image data transmission.

The advent of the metaverse has revolutionized virtual interactions and navigation, introducing intricate access control challenges. This paper addresses the need for effective access control models in the cloud-based metaverse. It explores its distinct characteristics, including its dynamic nature, diverse user base, and shared spaces, highlighting privacy concerns and legal implications. The paper analyzes access control principles specific to the cloud-based metaverse, emphasizing least privilege, separation of duties, RBAC, defense-in-depth, and auditability/accountability. It delves into identity verification and authorization methods, such as biometrics, multi-factor authentication, and role-based/attribute-based authorization. Advanced access control technologies for the cloud-based metaverse are examined, including SSO solutions, blockchain-based access control, ABAC, adaptive access control, and VMI for isolation. Risk mitigation strategies encompass IDS/IPS, SIEM, and user education programs.

As digital innovation advances, concerns surrounding privacy escalate. This manuscript explores the intricate relationship between machine learning and privacy preservation. Beginning with a comprehensive literature review, we delve into the current state of privacy in the digital age and examine machine learning’s role in addressing these concerns. The manuscript highlights key privacy-preserving techniques, including homomorphic encryption, differential privacy, and federated learning, providing in-depth insights into their applications and real-world implementations. Anticipating future challenges and trends, we recommend maintaining a delicate equilibrium between innovation and privacy in the dynamic landscape of machine learning.

The emergence of the metaverse, a virtual reality space, has ushered in a new era of digital experiences and interactions in global information systems. With its unique social norms and behaviors, this new world presents exciting opportunities for users to connect, socialize, and explore. However, as people spend more time in the metaverse, it has become increasingly apparent that the issue of cyberbullying needs to be addressed. Cyberbullying is a serious problem that can harm victims psychologically and physically. It involves using technology to harass, intimidate, or humiliate individuals or groups in global information systems. The risk of cyberbullying is high in the metaverse, where users are often anonymous. Therefore, it is crucial to establish a safer and more respectful culture within the metaverse to detect and prevent such incidents from happening.