The Privacy Paradox of Smart Environments: Reconciling Continuous Sensing with Privacy-Preserving Digital Intelligence

Smart environments increasingly rely on continuous sensing and artificial intelligence to interpret human activity, anticipate needs, and automate decisions across homes, healthcare facilities, workplaces, and public infrastructure. Although these capabilities can improve safety, responsiveness, and efficiency, persistent data collection also enables behavioral profiling, sensitive inference, secondary data use, and surveillance of individuals who may have limited control over monitoring. This narrative review critically examines the privacy intelligence tension by integrating technical, empirical, behavioral, and governance perspectives. It considers risks arising across the data lifecycle and evaluates the capabilities and limitations of data minimization, edge computing, federated learning, differential privacy, and cryptographic safeguards. The analysis highlights three recurring challenges: privacy guarantees that address isolated threats rather than cumulative inference, inconsistent measures of privacy and system utility, and insufficient attention to institutional power and the circumstances of affected individuals. It argues that privacy protection and intelligent functionality are not inherently incompatible, but their reconciliation depends on proportional sensing, clearly defined purposes, realistic threat models, and accountable deployment. To integrate these requirements, the review proposes a Context-Adaptive Privacy–Intelligence Assurance Framework linking necessity assessment, purpose-limited sensing, protected inference, context-sensitive action, and continuous oversight. The framework offers an agenda for future empirical evaluation rather than a validated solution, emphasizing longitudinal testing, bystander protection, and meaningful user control across diverse settings and changing technological and institutional conditions.