Zero-knowledge proofs (ZKPs) enable a prover to convince a verifier of knowledge of a secret without revealing it. The ZKP for the square-root problem has many applications in network and cloud security, such as user authentication and privacy-preserving cloud storage auditing. Classical protocols for the quadratic residuosity (square-root) relation require multiple iterations to reach negligible soundness error, incurring latency and communication costs that are critical in cloud settings. This paper proposes a new single-round zero-knowledge proof (SR-ZKP) for the square-root problem that achieves the same soundness as iterative schemes by increasing the challenge length. The protocol requires only one execution of a 4-message protocol (request, commit, challenge, response) and can be transformed into a one-message non-interactive ZKP via the Fiat–Shamir heuristic. The completeness, soundness, and zero-knowledge properties of the proposed scheme are formally proven. The results of this study show that the proposed protocol can achieve approximately \(97\%\) reduction in communication overhead and latency, when compared to an 80-round iterative ZKPs with RSA modulus n of size 2048 bits. This provides a substantial advantage for cloud applications.

Encrypted traffic classification has emerged as a critical component of modern network management and cloud security services. While Virtual Private Networks (VPNs) ensure user privacy by encrypting communications, this encryption also complicates traditional traffic identification. Recent research demonstrates that Machine Learning (ML) and Deep Learning (DL) techniques can effectively classify VPN versus non-VPN traffic even without payload inspection. However, to align with trustworthy cloud service requirements, these ML/DL approaches must also preserve user privacy and assure security. In this paper, we review the state-of-the-art ML/DL methods for encrypted VPN traffic classification, emphasizing techniques that enhance trust, including privacy-preserving federated learning, adversarially robust models, and explainable AI. In addition, this work aims to discover the most significant features affecting the VPN classification and identifying the best-performing ML and DL models on available VPN classification datasets. We include studies that focused on characterizing the VPN traffic besides classifying the secure traffic into VPN and non-VPN.

Trust in semantic Web is established by either credentials or reputation. Credential-based trust protocols assume the possession of credentials and transfer them between parties in order to establish trust. Since credentials can be private data, the act of providing private credentials implies poor privacy management even if transferred through secure (encrypted) channels. Exchanging private credentials is a major risk in critical applications since it eases unauthorized usage of private data. This paper presents a framework for safe trust protocols that interactively proves the possession of credentials without the need of exchanging them