As a PhD student, my research focuses on privacy challenges in Big Data, specifically for both structured and unstructured data. I explore methods to enhance data privacy while maintaining utility, examining advanced techniques such as differential privacy and anonymization techniques. My work aims to address the complexities of ensuring privacy in large-scale datasets across various domains, including text and databases used for Information Retrieval tasks. My work explores the intersection of deep learning models and data protection, addressing issues like private model training and inference on sensitive datasets, particularly in domains like healthcare and research pipelines. The goal is to develop innovative softwares and frameworks that balance privacy and performance in large-scale, data-driven applications.