A Multi-Level Privacy-Preserving Approach to Hierarchical Data Based on Fuzzy Set Theory

Nowadays, more and more applications are dependent on storage and management of semi-structured information. For scientific research and knowledge-based decision-making, such data often needs to be published, e.g., medical data is released to implement a computer-assisted clinical decision support system. Since this data contains individuals’ privacy, they must be appropriately anonymized before to be released. However, the existing anonymization method based on l-diversity for hierarchical data may cause a serious similarity attack, and cannot protect data privacy very well. In this paper, we utilize fuzzy sets to divide levels for sensitive numerical and categorical attribute values uniformly (a categorical attribute value can be converted into a numerical attribute value according to its frequency of occurrences), and then transform the value levels to sensitivity levels. The privacy model ( α l e v h , k)-anonymity for hierarchical data with multi-level sensitivity is proposed. Furthermore, we design a privacy-preserving approach to achieve this privacy model. Experiment results demonstrate that our approach is obviously superior to existing anonymous approach in hierarchical data in terms of utility and security.

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PFDLIS: Privacy-Preserving and Fair Deep Learning Inference Service under Publicly Verifiable Covert Security Setting

Electronics ◽

10.3390/electronics8121488 ◽

2019 ◽

Vol 8 (12) ◽

pp. 1488

Author(s):

Fengyi Tang ◽

Jialu Hao ◽

Jian Liu ◽

Huimei Wang ◽

Ming Xian

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Prediction Accuracy ◽

Data Privacy ◽

Input Data ◽

Computational Cost ◽

Privacy Preserving ◽

Model Parameters ◽

Privacy Model ◽

Security Properties

The recent popularity and widespread use of deep learning heralds an era of artificial intelligence. Thanks to the emergence of a deep learning inference service, non-professional clients can enjoy the improvements and profits brought by artificial intelligence as well. However, the input data of the client may be sensitive so that the client does not want to send its input data to the server. Similarly, the pre-trained model of the server is valuable and the server is unwilling to make the model parameters public. Therefore, we propose a privacy-preserving and fair scheme for a deep learning inference service based on secure three-party computation and making commitments under the publicly verifiable covert security setting. We demonstrate that our scheme has the following desirable security properties—input data privacy, model privacy and defamation freeness. Finally, we conduct extensive experiments to evaluate the performance of our scheme on MNIST dataset. The experimental results verify that our scheme can achieve the same prediction accuracy as the pre-trained model with acceptable extra computational cost.

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An Intelligent Computer-Assisted Instruction System Designed for Rural Health Workers in Developing Countries

Methods of Information in Medicine ◽

10.1055/s-0038-1634870 ◽

1992 ◽

Vol 31 (03) ◽

pp. 193-203 ◽

Cited By ~ 1

Author(s):

B. Auvert ◽

V. Gilbos ◽

F. Andrianiriana ◽

W. E. Bertrand ◽

X. Emmanuelli ◽

...

Keyword(s):

Developing Countries ◽

Decision Support ◽

Rural Health ◽

Health Workers ◽

Computer Assisted Instruction ◽

Computer Assisted ◽

Instruction System ◽

Knowledge Based ◽

Assisted Instruction ◽

Intelligent Computer

Abstract:This paper describes an intelligent computer-assisted instruction system that was designed for rural health workers in developing countries. This system, called Consult-EAO, includes an expert module and a coaching module. The expert module, which is derived from the knowledge-based decision support system Tropicaid, covers most of medical practice in developing countries. It allows for the creation of outpatient simulations without the help of a teacher. The student may practice his knowledge by solving problems with these simulations. The system gives some initial facts and controls the simulation during the session by guiding the student toward the most efficient decisions. All student answers are analyzed and, if necessary, criticized. The messages are adapted to the situation due to the pedagogical rules of the coaching module. This system runs on PC-compatible computer.

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Computer-Assisted Categorizing of Head Computed Tomography Reports for Clinical Decision Rule Research

Annals of Emergency Medicine ◽

10.1016/j.annemergmed.2006.06.031 ◽

2006 ◽

Vol 48 (5) ◽

pp. 551-557.e25

Author(s):

Stephen P. Wall ◽

Oliver Mayorga ◽

Christine E. Banfield ◽

Mark E. Wall ◽

Ilan Aisic ◽

...

Keyword(s):

Computed Tomography ◽

Decision Rule ◽

Clinical Decision ◽

Computer Assisted ◽

Clinical Decision Rule ◽

Head Computed Tomography

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Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

npj Digital Medicine ◽

10.1038/s41746-021-00431-6 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Qi Dou ◽

Tiffany Y. So ◽

Meirui Jiang ◽

Quande Liu ◽

Varut Vardhanabhuti ◽

...

Keyword(s):

Data Privacy ◽

Medical Training ◽

External Validation ◽

Mainland China ◽

Privacy Preserving ◽

Generalization Capability ◽

Major Focus ◽

Sensitive Data ◽

Lung Abnormalities ◽

Medical Image Diagnosis

AbstractData privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.

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Deep Transfer Learning for Improved Detection of Keratoconus using Corneal Topographic Maps

Cognitive Computation ◽

10.1007/s12559-021-09880-3 ◽

2021 ◽

Author(s):

Ali H. Al-Timemy ◽

Nebras H. Ghaeb ◽

Zahraa M. Mosa ◽

Javier Escudero

Keyword(s):

Transfer Learning ◽

Learning Strategy ◽

Clinical Decision ◽

Topographic Maps ◽

Computer Assisted ◽

Clinical Knowledge ◽

Average Accuracy ◽

Assisted Diagnosis ◽

Improved Accuracy ◽

Fine Tune

Abstract Clinical keratoconus (KCN) detection is a challenging and time-consuming task. In the diagnosis process, ophthalmologists must revise demographic and clinical ophthalmic examinations. The latter include slit-lamb, corneal topographic maps, and Pentacam indices (PI). We propose an Ensemble of Deep Transfer Learning (EDTL) based on corneal topographic maps. We consider four pretrained networks, SqueezeNet (SqN), AlexNet (AN), ShuffleNet (SfN), and MobileNet-v2 (MN), and fine-tune them on a dataset of KCN and normal cases, each including four topographic maps. We also consider a PI classifier. Then, our EDTL method combines the output probabilities of each of the five classifiers to obtain a decision based on the fusion of probabilities. Individually, the classifier based on PI achieved 93.1% accuracy, whereas the deep classifiers reached classification accuracies over 90% only in isolated cases. Overall, the average accuracy of the deep networks over the four corneal maps ranged from 86% (SfN) to 89.9% (AN). The classifier ensemble increased the accuracy of the deep classifiers based on corneal maps to values ranging (92.2% to 93.1%) for SqN and (93.1% to 94.8%) for AN. Including in the ensemble-specific combinations of corneal maps’ classifiers and PI increased the accuracy to 98.3%. Moreover, visualization of first learner filters in the networks and Grad-CAMs confirmed that the networks had learned relevant clinical features. This study shows the potential of creating ensembles of deep classifiers fine-tuned with a transfer learning strategy as it resulted in an improved accuracy while showing learnable filters and Grad-CAMs that agree with clinical knowledge. This is a step further towards the potential clinical deployment of an improved computer-assisted diagnosis system for KCN detection to help ophthalmologists to confirm the clinical decision and to perform fast and accurate KCN treatment.

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