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Temporal Differential Privacy for Human Activity Recognition
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS. Qamcom Research and Technology AB Stockholm, Sweden.ORCID iD: 0000-0001-6780-7755
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS. Research Institutes of Sweden (RISE) Stockholm, Sweden.ORCID iD: 0000-0003-4516-7317
2023 (English)Conference paper, Published paper (Refereed)
Abstract [en]

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 quantity 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 machine-learning/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-of-the-art approaches.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2023. p. 1-10
Keywords [en]
Differential Privacy, Machine Learning, Human Activity Recognition
National Category
Engineering and Technology
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-339761DOI: 10.1109/DSAA60987.2023.10302475OAI: oai:DiVA.org:kth-339761DiVA, id: diva2:1812849
Conference
2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), Thessaloniki, Greece, 9 - 13 October 2023
Funder
EU, Horizon 2020, 813162
Note

Part of ISBN 979-8-3503-4503-2

QC 20231117

Available from: 2023-11-17 Created: 2023-11-17 Last updated: 2024-02-07Bibliographically approved
In thesis
1. Towards Trustworthy Machine Learning For Human Activity Recognition
Open this publication in new window or tab >>Towards Trustworthy Machine Learning For Human Activity Recognition
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Human Activity Recognition presents a multifaceted challenge, encompassing the complexity of human activities, the diversity of sensors used, and the imperative to safeguard user data privacy. Recent advancements in machine learning, deep learning, and sensor technology have opened up new possibilities for human activity recognition. Wearable sensor-based human activity recognition involves collecting time-series data from various sensors, capturing intricate aspects of human activities. The focus of the above activity recognition problem is classifying human activities from the time-series data. Hence, this time-series classification problem demands efficient utilization of temporal properties. Moreover, while accurate prediction is crucial in human activity recognition, the reliability of predictions often goes unnoticed. Ensuring that predictions are reliable involves addressing two issues: calibrating miscalibrated predictions that fail to accurately represent the true likelihood of the data and addressing the challenges around uncertain predictions. Modern deep learning models, used extensively in human activity recognition, often struggle with the above issues. In addition to reliability concerns, machine learning algorithms employed in Human Activity Recognition are also plagued by privacy issues stemming from the utilization of sensitive activity data during model training. While existing techniques such as federated learning can provide some degree of privacy protection in these scenarios, they tend to adhere to a uniform concept of privacy and lack quantifiable privacy metrics that can be effectively conveyed to users and customized to cater to their individual privacy preferences. Hence, in the thesis, we identify the challenges around the effective use of temporal data, reliability, and privacy issues of machine learning models used for wearable sensor-based human activity recognition. To tackle these challenges, we put forth novel solutions, striving to enhance the overall performance and trustworthiness of machine learning models employed in human activity recognition.

Firstly, to improve classification performance, we propose a new temporal ensembling framework that uses data temporality effectively. The framework accommodates various window sizes for time-series data and trains an ensemble of deep-learning models based on that. It enhances classification accuracy and preserves temporal information.

Secondly, we address reliability through calibration and uncertainty estimation. The aforementioned temporal ensembling framework is used for calibration and uncertainty estimation. It provides well-calibrated predictions for human activity recognition and detects out-of-distribution activities, an important task of uncertainty estimation. Furthermore, we apply these methods to real-world scenarios, enhancing the reliability of human activity recognition models.

Thirdly, to address the privacy concern, we introduce a differentially private framework for time-series human activity recognition, quantifying privacy. Additionally, we develop a collaborative federated learning framework, allowing users to define their privacy preferences, advancing privacy preservation in human activity recognition.

These contributions address major challenges and promote improved classification, reliability, and privacy preservation in human activity recognition. It helps us to move towards trustworthy machine learning in human activity recognition, facilitating their usage in realistic and practical scenarios.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2024. p. xii, 56
Series
TRITA-EECS-AVL ; 2024:12
National Category
Computer Sciences
Research subject
Computer Science; Information and Communication Technology
Identifiers
urn:nbn:se:kth:diva-343130 (URN)978-91-8040-826-4 (ISBN)
Public defence
2024-03-06, https://kth-se.zoom.us/j/63687967257, Sal C, Kistagången 16, Kista, Stockholm, 13:00 (English)
Opponent
Supervisors
Funder
EU, Horizon 2020, 813162
Note

QC 20240207

Available from: 2024-02-07 Created: 2024-02-07 Last updated: 2024-02-29Bibliographically approved

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Roy, DebadityaGirdzijauskas, Sarunas

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