In an era defined by the exponential growth of data and increasing reliance on distributed systems, the concept of a privacy-preserving data fabric has emerged as a pivotal innovation in modern data architecture. This chapter explores the critical intersection between confidentiality and utility in data-driven environments where safeguarding sensitive information must be balanced against the imperative for analytical insight and data usability. Data fabric, as a unified architecture that facilitates secure and efficient access to distributed data, necessitates a multilayered approach to privacy—incorporating encryption, access control, anonymization, and differential privacy mechanisms. The core challenge lies in maintaining data utility—the ability to extract meaningful, actionable insights—while enforcing robust privacy protections that comply with ethical standards and regulatory mandates like GDPR, HIPAA, and CCPA. This chapter delves into privacy-preserving methodologies embedded in data fabrics, examining the trade-offs involved, the role of advanced technologies such as federated learning, homomorphic encryption, and secure multiparty computation, and the operationalization of privacy-aware machine learning pipelines. Additionally, the chapter addresses the organizational and ethical dimensions of data governance, data minimization, and the evolving landscape of data stewardship. Through practical case studies and architectural models, we propose a framework for decision-making that helps architects, data scientists, and compliance officers navigate this dynamic space. The goal is to strike a sustainable balance—where privacy does not come at the expense of progress, and data-driven innovation remains aligned with trust and accountability.