Differential Privacy Frameworks for Ethical Data Analytics

Abstract

Differential privacy (DP) provides a mathematically rigorous framework for privacy-preserving data analytics -- guaranteeing that any individual's participation in a dataset changes the probability of any analysis output by at most a multiplicative factor determined by the privacy parameter epsilon. As DP transitions from academic theory to practical deployment in industry and government, a set of ethical questions arises that the mathematical framework alone cannot resolve: how should epsilon be chosen and communicated to data subjects? Who bears the privacy cost of the epsilon budget? How should the privacy-utility trade-off be governed? This paper proposes the Differential Privacy Ethics Framework (DPEF), a systematic analysis of six ethical dimensions of DP deployment -- epsilon governance, budget accountability, utility-equity trade-offs, transparency and explainability, collective vs. individual privacy, and regulatory alignment -- applied to four DP deployment contexts: government statistics, health analytics, platform user data, and federated machine learning. DPEF introduces the DP Ethics Score (DPES) and evaluates three DP deployment approaches -- central DP, local DP, and federated DP -- against all six dimensions. Key results: federated DP achieves the highest DPES (0.892); epsilon governance is the most neglected ethical dimension across all current deployments; utility-equity trade-offs systematically disadvantage marginalised subgroups. The framework provides ethical DP deployment guidance for practitioners and regulators.