With the explosive growth of cloud-based services, massive amounts of personal and sensitive data are collected, stored, and processed on distributed platforms. While cloud infrastructures enable powerful big data analytics, they also raise pressing concerns about user privacy and data protection. Traditional security mechanisms such as encryption are effective against unauthorized access but fail to prevent privacy leakage during legitimate data analysis. This paper explores differential privacy, k-anonymity, and l-diversity as leading anonymization approaches to safeguard individual privacy while maintaining data utility. Through comparative analysis, the paper highlights strengths and limitations of these techniques and proposes a scalable anonymization framework tailored to cloud-based big data environments. The framework integrates differential privacy with classical anonymization strategies to achieve both robust privacy guarantees and efficient performance in large-scale, multi-tenant cloud systems.