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Kavach: A personalized secure and private decentralized learning setup for Human Activity Recognition
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0001-6780-7755
Qatar Computing Research Institute.
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0003-4516-7317
University of Insubria.
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Federated learning (FL) stands as a crucial method in preserving the data privacy of individuals who actively contribute to the machine learning task of Human Activity Recognition (HAR) through wearable devices.Although FL provides a degree of privacy protection, it's crucial to acknowledge that it may not always guarantee robust models and data privacy in specific scenarios. Moreover, FL typically ensures privacy by keeping data localized within user infrastructure, offering a uniform perspective on data privacy.To tackle these challenges, researchers are increasingly focusing on integrating privacy-preserving techniques like Differential Privacy (DP), Homomorphic Encryption (HE), and Trusted Execution Environments (TEE) into the FL framework. However, integrating these techniques directly can introduce their own set of challenges.For instance, DP, while effective in preserving privacy, can disrupt the learning process if excessive noise is added, which is particularly challenging in FL due to data heterogeneity. HE offers strong privacy guarantees but is constrained by computational complexity, and TEEs face scalability issues in practical implementations.To strike a balance between privacy and utility, we introduce the \emph{Kavach} framework designed to seamlessly integrate specific privacy-preserving methods while accommodating diverse privacy preferences. In this framework, privacy is recognized as a non-uniform concept, where privacy does not arise only by withholding data but also by incorporating different privacy preferences and privacy-preserving methods. With respect to the non-uniformity, the quantification of privacy by users plays a crucial role.Kavach leverages DP's unique ability to quantify privacy and tailor privacy budgets to individual user needs. Additionally, the framework allows integration of other privacy methods like encryption or TEEs on a limited scale, reserving them for specific requirements.The primary goal of \textit{Kavach} is to provide a personalized privacy experience for both clients and system designers. It offers flexibility, enabling users to choose their preferred privacy settings and methods, ensuring a customized and effective approach to privacy preservation within collaborative learning.In this work, we present different variants of the \textit{Kavach} framework and showcase experiments on benchmark Human Activity Recognition (HAR) datasets, as well as in the computer vision domain. These experiments highlight the privacy-utility tradeoff and classification performance, demonstrating the framework's effectiveness in achieving personalized privacy while maintaining utility.

Keywords [en]
Machine Learning, Differential Privacy, Federated Learning
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-342079OAI: oai:DiVA.org:kth-342079DiVA, id: diva2:1826238
Funder
EU, Horizon 2020, 813162
Note

QC 20240115

Available from: 2024-01-11 Created: 2024-01-11 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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