Privacy-preserving time series prediction with temporal convolutional neural networks

Falcetta, Alessandro; Roveri, Manuel

doi:10.1109/ijcnn55064.2022.9892823

Cited by 3 publications

(4 citation statements)

References 20 publications

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“…PINPOINT, a temporal convolutional neural network, was developed by Falcetta et al [116] to predict time series while maintaining anonymity. It is suggested to use a cloud-based forecasting system with integrated homomorphic encryption and temporal CNN.…”

Section: Encryption Algorithms For Deep Learning-based Medical Image ...mentioning

confidence: 99%

“…The efficacy of deep learningbased medical image analysis is demonstrated by a variety of recently published literature [12,[37][38][39][40][41]49,[49][50][51][52][53][54][55][56][57][58][59][60][61][62]64,64,65,[157][158][159]. In the literature, several deep learning architectures have been described to handle various imaging modalities and tasks related to medical image processing with different components of encryption or cryptography [107,110,[112][113][114][115][116]123,124,135,136,140,141,144,[147][148][149][150][151]160,161]. ...…”

Section: Various Deep Learning Architectures With Security Features F...mentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for Medical Image Cryptography: A Comprehensive Review

Lata

Cenkeramaddi

2023

Applied Sciences

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Electronic health records (EHRs) security is a critical challenge in the implementation and administration of Internet of Medical Things (IoMT) systems within the healthcare sector’s heterogeneous environment. As digital transformation continues to advance, ensuring privacy, integrity, and availability of EHRs become increasingly complex. Various imaging modalities, including PET, MRI, ultrasonography, CT, and X-ray imaging, play vital roles in medical diagnosis, allowing healthcare professionals to visualize and assess the internal structures, functions, and abnormalities within the human body. These diagnostic images are typically stored, shared, and processed for various purposes, including segmentation, feature selection, and image denoising. Cryptography techniques offer a promising solution for protecting sensitive medical image data during storage and transmission. Deep learning has the potential to revolutionize cryptography techniques for securing medical images. This paper explores the application of deep learning techniques in medical image cryptography, aiming to enhance the privacy and security of healthcare data. It investigates the use of deep learning models for image encryption, image resolution enhancement, detection and classification, encrypted compression, key generation, and end-to-end encryption. Finally, we provide insights into the current research challenges and promising directions for future research in the field of deep learning applications in medical image cryptography.

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Section: Encryption Algorithms For Deep Learning-based Medical Image ...mentioning

confidence: 99%

Section: Various Deep Learning Architectures With Security Features F...mentioning

confidence: 99%

Deep Learning for Medical Image Cryptography: A Comprehensive Review

Lata

Cenkeramaddi

2023

Applied Sciences

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“…10: Comparison of forecasting results between 8 models 0.83 (km/h), corresponding to n = 256 and n = 512, respectively. This is a remarkable improvement compared to previous studies [17], [19] that did not consider the data decomposition stage. This demonstrates the effectiveness of using data decomposition techniques such as EEMD in the forecasting process.…”

Section: B Combinationmentioning

confidence: 66%

“…On the other hand, working with big data has its own set of challenges, as discussed in studies [14]- [16]. Data leakage and targeted theft can occur, and therefore, post-quantum processing operations have been proposed and implemented [17]- [19]. These operations have shown significant effectiveness in increasing data security.…”

Section: Introductionmentioning

confidence: 99%

Quantum-Resistant Highly Accurate Wind Speed Prediction Model

Phan¹,

Tan²

2023

Preprint

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<p>This study aims to address the increasing complexity of cyber attacks and the sensitivity of wind speed data by exploring the combination of Artificial Intelligence (AI) and cyber security measures for improved wind speed prediction accuracy and security. The collected wind speed dataset is pre-processed and encrypted using the ring learning with errors (ring-LWE) encryption algorithm, which is known for its high level of security. The encrypted data is then input to the Ensemble Empirical Mode Decomposition (EEMD) to split the encrypted signal and reduce the impact of encryption noise. The combination of Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) models are also applied to each intrinsic mode function (IMFs) of the decomposition result. Furthermore, the parallel programming framework is also employed during the training phase, significantly improving the processing speed and efficiency. The results of the predictions are decrypted and compared with the testing set, and other works to evaluate the proposed model. The Mean Absolute Percent Error (MAPE) ranges from 7% to 10%, indicating a high level of accuracy. The findings of the study have practical implications for wind energy production and the development of smart grid systems. The study highlights the importance of considering both accuracy and security when using AI and suggests future directions for research in this area, including the development of more advanced encryption algorithms and the integration of AI and cyber security into real-world wind energy systems.</p>

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EVAD: encrypted vibrational anomaly detection with homomorphic encryption

Falcetta,

Roveri

2024

Neural Comput & Applic

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Privacy-preserving time series prediction with temporal convolutional neural networks

Cited by 3 publications

References 20 publications

Deep Learning for Medical Image Cryptography: A Comprehensive Review

Deep Learning for Medical Image Cryptography: A Comprehensive Review

Quantum-Resistant Highly Accurate Wind Speed Prediction Model

EVAD: encrypted vibrational anomaly detection with homomorphic encryption

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