Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System

Minopoulos, Georgios; Memos, Vasileios A.; Stergiou, Christos; Stergiou, Konstantinos D.; Plageras, Andreas P.; Koidou, Maria P.; Psannis, Kostas E.

doi:10.3390/app12125859

Cited by 21 publications

(12 citation statements)

References 58 publications

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“…On account of that the attacker needs to crack not just one image but a data set of images, he needs more computing power and more time to crack. Even if he uses the correct approach and kernel size, the probability that he recovers the entire image data set is 1 N * C! .…”

Section: Security Analysismentioning

confidence: 99%

“…When the user obtains the ciphertext feature maps and decrypts them, he can use the decrypted feature maps and the classical CNN to train a model or classify images in a local or a trusted server. In this stage, if the attacker has intercepted the ciphertext images and ciphertext feature maps (or plaintext feature maps), the probability that he recovers the entire image data set is 1 N * C! .…”

Section: Security Analysismentioning

confidence: 99%

“…In the era of data explosion, it has been very popular to store, analyze or process big data on the cloud server [1]. Although cloud server provides users with better computing power and resources, it also makes big data [2] face serious security issues, especially privacy issues.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid quantum–classical convolutional neural networks with privacy quantum computing

Huang¹,

Yan²,

Lin³

et al. 2023

Quantum Sci. Technol.

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Machine learning algorithms help us discover knowledge from big data. Data used for training or prediction often contain private information about users. Discovering knowledge while protecting data or user privacy is the way machine learning is expected, especially in the cloud environment. Quantum machine learning is a kind of machine learning that realizes parallel acceleration by quantum superposition. Quantum computing power for quantum machine learning is typically provided by quantum cloud computing services. Existing quantum machine learning algorithms hardly consider privacy protection. This paper presents an encryption method for image data which can effectively protect the input data privacy in hybrid quantum-classical convolutional neural networks algorithm. The user's original image data is first encrypted, and then sent to the quantum cloud to calculate the image convolution. By doing so, the feature map of the ciphertext image is obtained by the user. The result obtained by decrypting the feature map is the same as that obtained by using the original image as the input of convolution calculation. Experiments show that our privacy protection scheme can protect the privacy of input image data in the hybrid quantum-classical neural networks algorithm, but does not affect the accuracy of the algorithm. In addition to image encryption and feature map decryption, the proposed scheme does not bring additional computational complexity.

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Section: Security Analysismentioning

confidence: 99%

Section: Security Analysismentioning

confidence: 99%

See 1 more Smart Citation

Hybrid quantum–classical convolutional neural networks with privacy quantum computing

Huang¹,

Yan²,

Lin³

et al. 2023

Quantum Sci. Technol.

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show abstract

“…The classification is a type of supervised learning method [70][71][72][73]. Recently, there are many applications of machine learning classification algorithms detailed in [69][70][71][72][73][74][75]. It is a categorization method that distinguishes, predicts, and understands objects and ideas.…”

Section: 7classificationmentioning

confidence: 99%

Improving Lungs Cancer Detection Based on Hybrid Features and Employing Machine Learning Techniques

Yang

Por

Khan

et al. 2023

Preprint

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Lung cancer detection using machine learning involves training a model on a dataset of medical images, such as CT scans, to identify patterns and features associated with lung cancer. Past researchers developed different computer aided diagnostic (CAD) systems for early prediction of lung cancer. The researchers extracted single features such as texture, morphology etc.; however, by combining the features, accuracy can be improved. In this study, we extracted Gray-level co-occurrence (GLCM), autoencoder and Haralick texture features. We combined these features and computed the performance using robust machine algorithms including Decision tree (DT), Naïve Bayes (NB) and support vector machine (SVM) with different kernel functions. The performance was evaluated using standard performance measures. The hybrid methods such as GLCM + Autoencoder, and Haralick + Autoencoder yielded highest detection performance using SVM Gaussian and radial base function (RBF) with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) with accuracy of 100% and AUC 1.00 followed by SVM polynomial yielded an accuracy of 99.89% and AUC of 1.00; GLCM + Haralick using SVM Gaussian yielded accuracy (99.56%), SVM RBF yielded accuracy (99.35%). The results reveal that the proposed feature extraction methodology can be usefully used to predict the lung cancer for further diagnosis at early stage. MSC: Artificial Intelligence, Machine Learning, Lung Cancer, cross validation

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“…The IoT has the ability to collect information from embedded devices in the environment and to process these data to achieve desired goals. Such internet and cloud-connected devices hold great promise for improved smart health care solutions [ 20 ]. Here, we propose a novel, specially designed IoT sensor that can be plugged into the user’s monitor and communicates with a server to determine, in real time, user changes in posture, e.g., sitting or standing events, and their presence at their desk.…”

Section: Introductionmentioning

confidence: 99%

A Privacy-Preserving Desk Sensor for Monitoring Healthy Movement Breaks in Smart Office Environments with the Internet of Things

Maiti

Schmidt

et al. 2023

Sensors

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Smart workplace Internet of Things (IoT) solutions rely on several sensors deployed efficiently in the workplace environment to collect accurate data to meet system goals. A vital issue for these sensor-based IoT solutions is privacy. Ideally, the occupants must be monitored discreetly, and the strategies for maintaining privacy are dependent on the nature of the data required. This paper proposes a new sensor design approach for IoT solutions in the workplace that protects occupants’ privacy. We focus on a novel sensor that autonomously detects and captures human movements in the office to monitor a person’s sedentary behavior. The sensor guides an eHealth solution that uses continuous feedback about desk behaviors to prompt healthy movement breaks for seated workers. The proposed sensor and its privacy-preserving characteristics can enhance the eHealth solution system’s performance. Compared to self-reporting, intrusive, and other data collection techniques, this sensor can collect the information reliably and timely. We also present the data analysis specific to this new sensor that measures two physical distance parameters in real-time and uses their difference to determine human actions. This architecture aims to collect precise data at the sensor design level rather than to protect privacy during the data analysis phase.

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Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System

Cited by 21 publications

References 58 publications

Hybrid quantum–classical convolutional neural networks with privacy quantum computing

Hybrid quantum–classical convolutional neural networks with privacy quantum computing

Improving Lungs Cancer Detection Based on Hybrid Features and Employing Machine Learning Techniques

A Privacy-Preserving Desk Sensor for Monitoring Healthy Movement Breaks in Smart Office Environments with the Internet of Things

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