Enhanced Human Activity Recognition based on Activity Tracker Data Using Secure Homomorphic Encryption Techniques

K, Anitha Kumari; Indusha, M.; Dharani, D.

doi:10.1109/incet51464.2021.9456309

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…Despite its reputation for computational complexity, homomorphic encryption (HE) has gained attention among researchers, resulting in the fusion of HAR and HE in several recent studies [39,13,14,28]. To mitigate the computational challenges associated with HE, Zhou et al [39] introduce partial homomorphic encryption as a viable solution, while Kim et al [13] employ HE specifically in the inference stage.…”

Section: Privacy Preservation In Human Activity Recognition While a G...mentioning

confidence: 99%

Kavach: A Personalized Secure and Private Decentralized Learning Setup for Human Activity Recognition

Roy,

Lekssays,

Girdzijauskas

et al. 2024

Preprint

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Section: Privacy Preservation In Human Activity Recognition While a G...mentioning

confidence: 99%

Kavach: A Personalized Secure and Private Decentralized Learning Setup for Human Activity Recognition

Roy,

Lekssays,

Girdzijauskas

et al. 2024

Preprint

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“…Sozinov et al [30] developed a federated learning algorithm for HAR, where the data is private to individual users. Kumari et al [18] used homomorphic encryption to guarantee the security and privacy of activity tracker data for HAR workloads. However, these methods lack the privacy accountability of differential privacy.…”

Section: Related Workmentioning

confidence: 99%

Temporal Differential Privacy for Human Activity Recognition

Roy,

Girdzijauskas

2023

2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)

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Differential privacy (DP) is a method to protect individual privacy when the data is used for downstream analytical tasks. The core ability of DP to quantify privacy numerically separates it from other privacy-preserving methods. In human activity recognition (HAR), differential privacy can protect users' privacy who contribute their data to train machine learning algorithms. While some methods are developed for privacy protection in such cases, no method quantifies privacy and seamlessly integrates into machine learning frameworks like DP. The paper proposes a DP framework called TEMPDIFF (short for temporal differential privacy), which guarantees privacy preserving human activity recognition for wearable time-series data with competitive classification performance and works with any machinelearning/deep-learning methods. TEMPDIFF capitalizes on the temporal characteristics of wearable sensor data to improve the modelling task, which enhances the privacy-utility tradeoff. TEMPDIFF uses ensembling and a novel temporal partitioning algorithm for time-series data to ensure optimal training of ensemble models. In TEMPDIFF, consensus through ensembling and the addition of controlled Laplacian noise obscures sensitive information used to train the models, guaranteeing strict levels of differential privacy. The proposed method is evaluated on two popular HAR datasets. It outperforms the classification accuracy and privacy budget for both datasets compared to the state-ofthe-art approaches.

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