Organizations increasingly integrate and share person-level data across internal platforms and external partners to enable analytics, digital services, and evidence-based decision making. However, combining quasi-identifiers across systems and releases can enable re-identification via linkage attacks, creating regulatory compliance and trust risks. This paper proposes an operational methodology for (i) identifying direct identifiers and quasi-identifiers (QIs), (ii) quantifying baseline re-identification risk using uniqueness and prosecutor-style risk proxies, and (iii) applying Local Differential Privacy (LDP) to reduce link-ability prior to data sharing. We implement categorical LDP using a Generalized Randomized Response (GRR) mechanism and evaluate privacy–utility trade-offs through a sensitivity analysis over the privacy budget ε. Utility is quantified using (a) distributional distortion (total variation distance) and (b) downstream task performance (job-title classification). We further address reviewer concerns by discussing repeated releases, privacy accounting as mitigations for longitudinal deployments, and by improving figure readability and updating related work with recent studies.
Randa Aljably (Mon,) studied this question.