A Privacy-Preserving Identification Mechanism for Mobile Sensing Systems

Niu, Xiaoguang; Ye, Qiongzan; Zhang, Yihao; Ye, Dengpan

doi:10.1109/access.2018.2803129

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…Because anonymizing the data set several times is not enough to protect the data from a solid and well-prepared attacker, for example, in an n-element database, a specific feature knower of n−1 objects can easily infer the value of the individual attribute that remains, and in this research, we use differential privacy, which is an interactive method that protects data, even from attackers with prior knowledge of it [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

Privacy-Preserving Sports Wearable Data Fusion Framework

Zhang

2022

Computational Intelligence and Neuroscience

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When the sports industry has access to advanced training and preparation techniques, the sports sector is entering a new era, where real-time data processing services have a crucial priority in improving physical fitness and avoiding injuries to athletes. The primary sports support methodology is based on multiple sensors, mainly wearables, often of different types and technology, which collect somatometric data in real time and are usually analyzed with deep learning technologies. And while modern athletes train and prepare intelligently using the innovative techniques of available technology, there is considerable concern about the use of personal data. There is great concern about cyberattacks and possible data leaks that could affect the sports industry and sports in general. To secure the personal data of athletes collected and analyzed by sports wearables, this paper presents a privacy-preserving sports wearable data fusion framework. This is an advanced methodology based on Lagrange's relaxation method for the problem of multiple assignments and synthesis of information by numerous sensors and the use of differential privacy to access databases with personal information, ensuring that this information will remain personal without a third entity may disclose the identity of the athlete who provided the data.

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Section: Methodsmentioning

confidence: 99%

Privacy-Preserving Sports Wearable Data Fusion Framework

Zhang

2022

Computational Intelligence and Neuroscience

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“…By avoiding the need to store data in a single location, we can harness multiple data sources, safeguard individual privacy, and reduce data storage expenses while achieving high accuracy levels [72,73]. Access to a large network of data from sources scattered over multiple data centers benefits the deep learning models [74,75]. Manoj et al [76] apply federated learning to predict agriculture production using weather data, soil data, and crop management data collected from numerous data silos.…”

Section: Deep Imbalanced Learningmentioning

confidence: 99%

Federated Learning with GANs-based Synthetic Minority Over-sampling Technique for Improving Weather Prediction from Imbalanced Data

Jafarigol

Trafali̇s

2023

Preprint

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Detecting rare events is a challenging task among machine learning practitioners, motivating them to navigate and further improve data processing and algorithmic approaches to find accurate and computationally efficient methods for imbalanced learning. Imbalanced data is common in weather prediction, where the massive size of data poses storage and computational challenges. To learn from imbalanced data, the algorithms must strive to learn each class precisely to be able to classify both the minority and the majority class. While creating complex models is somewhat necessary for such problems, the machine learning algorithm must have adequate generalization capabilities. In recent years, federated learning has been used as an environmentally-friendly approach, which can produce accurate results in a distributed setting. Although privacy is not a concern of this study, inherent characteristics of federated learning are beneficial in weather prediction. Deep learning combined with data augmentation is explored in the framework of federated learning. We compare multiple data augmentation methods in a centralized and federated learning framework. Federated learning has great potential for weather prediction tasks. By leveraging data from multiple sources, we were able to improve the accuracy and generalization of the classifier. Addressing the issue of imbalanced data is an essential step. Incorporating the two remarkable approaches of Generative Adversarial Networks and Synthetic Minority Over-sampling Technique is a suitable solution for tabular weather data, and our experimental results confirm the effectiveness of this approach.

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“…By avoiding the need to store data in a single location, we can harness multiple data sources, safeguard individual privacy, and reduce data storage expenses while achieving high accuracy levels [25,26]. Access to a large network of data from sources scattered over multiple data centers benefits the deep learning models [27,28]. Class imbalance appears in many centralized and federated machine learning problems.…”

Section: Federated Learningmentioning

confidence: 99%

A Distributed Approach to Meteorological Predictions: Addressing Data Imbalance in Precipitation Prediction Models through Federated Learning and GANs

Jafarigol,

Trafalis

2023

Preprint

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The classification of weather data involves categorizing meteorological phenomena into classes, thereby facilitating nuanced analyses and precise predictions for various sectors such as agriculture, aviation, and disaster management. This involves utilizing machine learning models to analyze large, multidimensional weather datasets for patterns and trends. These datasets may include variables such as temperature, humidity, wind speed, and pressure, contributing to meteorological conditions. Furthermore, it’s imperative that classification algorithms proficiently navigate challenges such as data imbalances, where certain weather events (e.g., storms or extreme temperatures) might be underrepresented. This empirical study explores data augmentation methods to address imbalanced classes in tabular weather data in centralized and federated settings. Employing data augmentation techniques such as the Synthetic Minority Over-sampling Technique or Generative Adversarial Networks can improve the model's accuracy in classifying rare but critical weather events. Moreover, with advancements in federated learning, machine learning models can be trained across decentralized databases, ensuring privacy and data integrity while mitigating the need for centralized data storage and processing. Thus, the classification of weather data stands as a critical bridge, linking raw meteorological data to actionable insights, enhancing our capacity to anticipate and prepare for diverse weather conditions.

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A Privacy-Preserving Identification Mechanism for Mobile Sensing Systems

Cited by 9 publications

References 24 publications

Privacy-Preserving Sports Wearable Data Fusion Framework

Privacy-Preserving Sports Wearable Data Fusion Framework

Federated Learning with GANs-based Synthetic Minority Over-sampling Technique for Improving Weather Prediction from Imbalanced Data

A Distributed Approach to Meteorological Predictions: Addressing Data Imbalance in Precipitation Prediction Models through Federated Learning and GANs

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