The growing reliance on digital governance and data-centric research has amplified the demand for safe information-sharing protocols. Public and private entities routinely publish unit-level datasets (microdata) to drive policy formulation and analytical studies. Nevertheless, the distribution of this data introduces severe privacy challenges, particularly the threat of exposing individual identities. While data anonymization aims to balance privacy with analytical utility, traditional masking methods are no longer sufficient against advanced computational linkage attacks and maximum-knowledge threat models. This paper investigates the progression from basic data masking to sophisticated Privacy-Enhancing Technologies (PETs), notably Differential Privacy and Statistical Disclosure Control (SDC). Furthermore, it analyses the legal and ethical landscape shaped by India’s Digital Personal Data Protection (DPDP) Act, 2023, specifically addressing the "accountability paradox" where fully anonymized data loses regulatory protection. To practically evaluate the privacy-utility trade-off, this study incorporates a simulated experiment using a synthetic microdata set modelled after the National Sample Survey's Periodic Labour Force Survey (PLFS). By applying k-anonymity to demographic quasi-identifiers and injecting controlled algorithmic noise (Differential Privacy) into sensitive continuous variables like income, the research systematically quantifies the margin of analytical error introduced by varying privacy budgets ( ). The practical implementation demonstrates how data curators can utilize tools like the sdcMicro framework to precisely measure information loss. Ultimately, the study concludes that technical mechanisms alone are insufficient. It proposes a holistic framework that merges algorithmic privacy guarantees with interactive parameter visualization, strict institutional oversight, and legal accountability to safely leverage microdata in the digital economy.

Keywords— Data Anonymization; Differential Privacy; Statistical Disclosure Control; Microdata Privacy; Re-identification Risk; Data Governance.