Differential Privacy at a TechnologySociety Lens: The Critical Role of Privacy Policies

The rapid expansion of data collection and analysis has intensified societal concerns regarding personal privacy. Although differential privacy (DP) provides rigorous mathematical guarantees, its practical deployment requires closer alignment with user rights, evolving privacy norms, and public policy principles. This article addresses the gap between DP’s technical mechanisms and broader socio-ethical expectations by introducing user-defined privacy and secret specifications as formalized representations of personalized privacy requirements. Building on this foundation, we propose the policy-driven DP (PDDP) model, which integrates these specifications directly into the DP framework, enabling user-centric and policy-responsive privacy protection. We develop a tailored randomized response mechanism to operationalize PDDP, providing fine-grained and adaptable privacy protection. Experimental results on real datasets demonstrate that PDDP delivers improved privacy-utility tradeoffs while accommodating heterogeneous user preferences. Beyond technical results, we discuss how PDDP strengthens user trust and promotes alignment between privacy technologies and regulatory and societal trends. Finally, we outline several future research directions, including extending PDDP to decentralized settings, expanding supported query types, and examining its role in adaptive governance and evolving privacy policies.