A Comprehensive Review of Probabilistic Analysis of: Threshold Cryptography for Cloud Storage: Security Models, Optimization Techniques, and Emerging Computing Applications

Abstract

Threshold cryptography has emerged as a fundamental paradigm for securing distributed cloud storage systems by enabling collaborative key management without exposing complete cryptographic secrets to any single entity. However, the probabilistic behavior of threshold schemes, particularly under adversarial conditions and large-scale cloud deployments, remains insufficiently explored. This paper presents a comprehensive review of probabilistic analysis techniques applied to threshold cryptography for cloud storage, focusing on security models, optimization strategies, and emerging computing applications. The study synthesizes recent advancements between 2018 and 2025, examining how probabilistic models enhance resilience against failures, collusion, and adaptive attacks. Furthermore, the integration of chaotic systems and generative artificial intelligence is analyzed for improving entropy, key generation, and adaptive security mechanisms. The findings reveal that while threshold schemes significantly enhance fault tolerance and confidentiality, challenges persist in scalability, computational overhead, and real-time adaptability. This review contributes by identifying research gaps, proposing future directions, and bridging theoretical cryptographic models with modern software engineering practices.