Data Privacy and Security in Big Data: A Comparative Analysis

The rapid proliferation of big data and data-driven decision-making has brought about unprecedented technological advancements, revolutionizing various industries. Organizations now harness extensive data to extract valuable insights, optimize operations, and enhance customer experiences. However, this data-driven landscape raises concerns about individual privacy and data security. As personal information collection and analysis become more prevalent, robust privacy approaches are imperative. This research paper conducts a comparative analysis of privacy approaches in the context of big data and data-driven decision-making. It examines traditional methods such as cryptography, anonymization, and access controls, alongside emerging techniques like differential privacy, homomorphic encryption, and secure multi-party computation. Qualitative content analysis and thematic coding gather insights from academic literature, reports, and expert interviews. The findings highlight each approach’s strengths, limitations, and trade-offs, offering valuable insights for organizations aiming to balance data utility with privacy preservation. This study contributes to understanding ethical concerns, legal compliance, data quality, trust, and technological advancements in the pursuit of responsible data-driven decision-making while safeguarding privacy.