SCEM: A Structure-Preserving Privacy Framework for Sensor-Generated Time-Series Data

Intelligent sensor systems and Internet of Things (IoT) platforms continuously generate high-value time-series data for downstream analytics and operational monitoring. However, statistical, spectral, and distributional characteristics embedded in these sequences may inadvertently expose sensitive behaviors, operating states, or temporal routines. To address this issue, we propose Structured Constrained Energy Mapping (SCEM), a general and extensible framework for the feature-selective protection of sensor-generated time-series data. Its generality arises from a unified instantiation interface that supports customizable protection mechanisms for heterogeneous sensitive functionals. SCEM follows a four-stage operator workflow, namely feature mapping, structured perturbation, feature recovery, and utility-oriented calibration, with a generalized privacy energy representation supporting the transition from mapping to perturbation. We demonstrate the practical applicability of the framework through five representative instantiations covering spectral amplitudes, mean, variance, quantiles, and high-order moments. Experiments on two sensor-related monitoring benchmarks and one non-sensor temporal benchmark show that SCEM achieves an unweighted mean attack success rate at the 10% reporting threshold (ASR@10%) of 4.69% over the 18 reported SCEM feature–dataset pairs under the evaluated direct feature inference attacks, indicating the reduced recoverability of predefined feature-level information in this protocol. Meanwhile, SCEM maintains competitive forecasting-oriented utility compared with representative protection baselines and the original-data utility reference, achieving a favorable privacy–utility trade-off. Overall, the results suggest that SCEM can serve as a practical model-free preprocessing layer for reducing feature-level leakage while preserving useful temporal structures for intelligent sensor data sharing.