cs.LGMar 12, 2026

CFD-HAR: User-controllable Privacy through Conditional Feature Disentanglement

Authors: Alex Gn, Fan Li, S Kuniyilh, Ada Axan

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Abstract

Modern wearable and mobile devices are equipped with inertial measurement units (IMUs). Human Activity Recognition (HAR) applications running on such devices use machine-learning-based, data-driven techniques that leverage such sensor data. However, sensor-data-driven HAR deployments face two critical challenges: protecting sensitive user information embedded in sensor data in accordance with users' privacy preferences and maintaining high recognition performance with limited labeled samples. This paper proposes a technique for user-controllable privacy through feature disentanglement-based representation learning at the granular level for dynamic privacy filtering. We also compare the efficacy of our technique against few-shot HAR using autoencoder-based representation learning. We analyze their architectural designs, learning objectives, privacy guarantees, data efficiency, and suitability for edge Internet of Things (IoT) deployment. Our study shows that CFD-based HAR provides explicit, tunable privacy protection controls by separating activity and sensitive attributes in the latent space, whereas autoencoder-based few-shot HAR offers superior label efficiency and lightweight adaptability but lacks inherent privacy safeguards. We further examine the security implications of both approaches in continual IoT settings, highlighting differences in susceptibility to representation leakage and embedding-level attacks. The analysis reveals that neither paradigm alone fully satisfies the emerging requirements of next-generation IoT HAR systems. We conclude by outlining research directions toward unified frameworks that jointly optimize privacy preservation, few-shot adaptability, and robustness for trustworthy IoT intelligence.

Figures & tables

Fig. 1: Threat model
Fig. 2: CFD-HAR Architecture.
AspectCFD-HARFS-HAR
Primary ObjectiveUser-controllable privacyFew-shot learning
Learning Paradigmconditional disentanglementFew-shot
Latent RepresentationDisentanglementCompact but entangled
Label RequirementsRequires activity labels and often sensitive attribute labelsRequires few activity labels; no sensitive labels needed
Privacy ProtectionStrong and user-controllableLimited or absent
SustainabilityModerate computationHigh computation
TABLE I: Comparison Between Conditional Feature Disentanglement HAR and Few-Shot Autoencoder-Based HAR
Fig. 3: Activity classification performance against each private attribute preference.
Fig. 4: Re-identification performance against each private attribute preference.
 0000000100100011010001010110011110001001101010111100110111101111
Activity0.940.840.880.830.860.820.870.790.930.850.890.850.840.820.850.77
Identity0.790.240.190.180.210.310.220.230.330.270.170.190.250.190.130.11
TABLE II: Activity classification performance in F1-score based on various private activity combinations selected dynamically.
Fig. 5: Activity classification performance against each private attribute weight.
Fig. 6: Re-identification performance against each private attribute weight.

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