DIFFERENTIAL PRIVACY IN BIG DATA ANALYTICS: TECHNIQUES AND APPLICATIONS

• Dr. Amina Farooq

  Department of Data Science, University of Lahore, Lahore, Pakistan.

Differential privacy (DP) is a powerful privacy-preserving technique aimed at protecting individual data in statistical analyses. With the proliferation of big data in diverse sectors such as healthcare, finance, and social media, ensuring privacy without sacrificing data utility has become a key challenge. This article explores differential privacy's role in big data analytics, providing a detailed overview of its techniques, applications, and emerging trends. The paper also examines its real-world applications, including healthcare analytics, finance, and machine learning models, and discusses the challenges and limitations in implementing DP for large-scale data. Lastly, it presents future directions for integrating DP with advanced analytics frameworks to enhance privacy protection.

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