ABSTRACT Recent advances in edge computing enable data stream privacy enforcement directly on resource‐constrained devices, reducing latency and the exposure of sensitive information. In this paper, we extend and validate our previously proposed privacy‐enforcing framework, which allows high‐level privacy policies to be expressed as chains of triggers and transformations, executed at the edge. To assess its practical viability, we conduct a comprehensive performance profiling of multiple privacy models across heterogeneous edge hardware platforms. Six privacy‐model chains, ranging from basic face detection to combined face‐and‐person anonymization, are evaluated across three representative edge devices. Key performance metrics (i.e., execution time, CPU utilization, memory usage, and power consumption) are measured to inform optimal placement of privacy transformations. Our evaluation offers critical insights into the effectiveness of the privacy‐enforcing framework on resource‐constrained devices, thereby guiding practitioners in selecting suitable deployment targets for privacy‐preserving data stream analytics on the edge.
Murturi et al. (Wed,) studied this question.