Privacy-preserving Time-series Medical Images Analysis Using a Hybrid Deep Learning Framework

Yue, Zijie; Ding, Shuai; Zhao, Lei; Zhang, Youtao; Cao, Zehong; Tanveer, M.; Jolfaei, Alireza; Zheng, Xi

doi:10.1145/3383779

Cited by 31 publications

(11 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method will minimize model parameter leakage and prevent model parameter sharing. Yue et al (2021) proposed a hybrid approach of HE + modifications in activation functions of the network for time series medical image analysis using LSTM. Boura et al (2018) protected the training data privacy by approximating the ReLU activation function to a low degree polynomial with mean pooling layers.…”

Section: Differential Privacy-based Applicationsmentioning

confidence: 99%

Privacy‐preserving data mining and machine learning in healthcare: Applications, challenges, and solutions

Naresh¹,

Thamarai²

2023

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Data mining (DM) and machine learning (ML) applications in medical diagnostic systems are budding. Data privacy is essential in these systems as healthcare data are highly sensitive. The proposed work first discusses various privacy and security challenges in these systems. To address these next, we discuss different privacy-preserving (PP) computation techniques in the context of DM and ML for secure data evaluation and processing. The state-of-the-art applications of these systems in healthcare are analyzed at various stages such as data collection, data publication, data distribution, and output phases regarding PPDM and input, model, training, and output phases in the context of PPML. Furthermore, PP federated learning is also discussed. Finally, we present open challenges in these systems and future research directions.

show abstract

Section: Differential Privacy-based Applicationsmentioning

confidence: 99%

Privacy‐preserving data mining and machine learning in healthcare: Applications, challenges, and solutions

Naresh¹,

Thamarai²

2023

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…Despite the benefit of provable privacy guarantees, the range of operations available in HE is restricted to addition and multiplication i.e., fully homomorphic encryption. This limits the set and number of transformations applicable to the data and requires the use of approximations for more complex operations (e.g., HE-ReLU is the polynomial approximation of the ReLU function (Yue et al, 2021b)). This also significantly increases the computational time needed to process encrypted text compared to plaintext by several orders of magnitude (Popescu et al, 2021).…”

Section: Homomorphic Encryptionmentioning

confidence: 99%

Privacy-preserving machine learning for healthcare: open challenges and future perspectives

Guerra-Manzanares¹,

Lopez²,

Maniatakos³

et al. 2023

Preprint

View full text Add to dashboard Cite

Machine Learning (ML) has recently shown tremendous success in modeling various healthcare prediction tasks, ranging from disease diagnosis and prognosis to patient treatment. Due to the sensitive nature of medical data, privacy must be considered along the entire ML pipeline, from model training to inference. In this paper, we conduct a review of recent literature concerning Privacy-Preserving Machine Learning (PPML) for healthcare. We primarily focus on privacy-preserving training and inference-as-a-service, and perform a comprehensive review of existing trends, identify challenges, and discuss opportunities for future research directions. The aim of this review is to guide the development of private and efficient ML models in healthcare, with the prospects of translating research efforts into real-world settings.

show abstract

“…Xu et al [32] presented a framework involving the learning of Multimodal Attention Long-Short Term Memory Networks (MA-LSTM) to enhance the automatic generation of video captions. Yue et al [33] proposed using the HE-CLSTM, a privacy-preserving, computer-aided diagnosis algorithm, to analyze encrypted time-series medical images. Woo et al [34] developed the CBAM, which can be effectively integrated into any CNN architecture.…”

Section: B Attention Mechanismsmentioning

confidence: 99%

Deep Learning Model for House Price Prediction Using Heterogeneous Data Analysis Along With Joint Self-Attention Mechanism

Wang

Chen

et al. 2021

IEEE Access

View full text Add to dashboard Cite

House price prediction is a popular topic, and research teams are increasingly performing related studies by using deep learning or machine learning models. However, because some studies have not considered comprehensive information that affects house prices, prediction results are not always sufficiently precise. Therefore, we propose an end to end joint self-attention model for house prediction. In this model, we import data on public facilities such as parks, schools, and mass rapid transit stations to represent the availability of amenities, and we use satellite maps to analyze the environment surrounding houses. We adopt attention mechanisms, which are widely used in image, speech, and translation tasks, to identify crucial features that are considered by prospective house buyers. The model can automatically assign weights when given transaction data. Our proposed model differs from self-attention models because it considers the interaction between two different features to learn the complicated relationship between features in order to increase prediction precision. We conduct experiments to demonstrate the performance of the model. Experimental data include actual selling prices in real estate transaction data for the period from 2017 to 2018, public facility data acquired from the Taipei and New Taipei governments, and satellite maps crawled using the Google Maps application programming interface. We utilize these datasets to train our proposed and compare its performance with that of other machine learning-based models such as Extreme Gradient Boosting and Light Gradient Boosted Machine, deep learning, and several attention models. The experimental results indicate that the proposed model achieves a low prediction error and outperforms the other models. To the best of our knowledge, we are the first research to incorporate attention mechanism and STN network to conduct house price prediction.

show abstract

Privacy-preserving Time-series Medical Images Analysis Using a Hybrid Deep Learning Framework

Cited by 31 publications

References 43 publications

Privacy‐preserving data mining and machine learning in healthcare: Applications, challenges, and solutions

Privacy‐preserving data mining and machine learning in healthcare: Applications, challenges, and solutions

Privacy-preserving machine learning for healthcare: open challenges and future perspectives

Deep Learning Model for House Price Prediction Using Heterogeneous Data Analysis Along With Joint Self-Attention Mechanism

Contact Info

Product

Resources

About