Differential Privacy for Data and Model Publishing of Medical Data

Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from onedimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding the impressive performance of ML/DL, there are still lingering doubts regarding the robustness of ML/DL in healthcare settings (which is traditionally considered quite challenging due to the myriad security and privacy issues involved), especially in light of recent results that have shown that ML/DL are vulnerable to adversarial attacks. In this paper, we present an overview of various application areas in healthcare that leverage such techniques from security and privacy point of view and present associated challenges. In addition, we present potential methods to ensure secure and privacy-preserving ML for healthcare applications. Finally, we provide insight into the current research challenges and promising directions for future research.

show abstract

“…[127], [149], [150] Highly practical, as no computational overhead is involved because no encryption is performed.…”

Section: Differential Privacymentioning

confidence: 99%

Secure and Robust Machine Learning for Healthcare: A Survey

Qayyum

Qadir

Bilal

et al. 2021

IEEE Rev. Biomed. Eng.

343

144

View full text Add to dashboard Cite

show abstract

“…The time taken to upload the dataset into the cloud is given with respect to increase in records and the increase in number of data analysts respectively. The previous work [19] using the CART method and deep neural network have reached nearer to DP-FCNN, where their difference is 5ms and 5s in their runtime for data processing and query processing. In contrast, all the other papers have higher runtime than this due to the absence of machine learning algorithms and use of poor parameters for preserving privacy.…”

Section: ) Scalabilitymentioning

confidence: 97%

“…The privacy level was estimated from the trust distance, measured from the Markov process. Then, in machine learning algorithms two approaches were developed to ensure privacy based on the estimation of weight using classification and regression tree (CART) method [19]. The noise to be added is computed using the attribute weights by decision tree.…”

Section: Problem Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

On Differential Privacy-Based Framework for Enhancing User Data Privacy in Mobile Edge Computing Environment

et al. 2021

View full text Add to dashboard Cite

The potential growth in data mining has an important aspect on security due to the consideration of the data as an asset. The provisioning of protection in a public infrastructure fails to ensure privacy disclosure of an individual's information. Differential Privacy (DP) is a promising solution for assuring privacy protection by injecting noise using the Laplace mechanism or Exponential mechanism. The access of data by analysts is performed via edge devices. A common problem identified from previous research work is the leakage of privacy at the edge layer and data accessed by unauthorized people. To address the problem, this paper proposes DP-FCNN, that implements Differential Privacy using a Fuzzy Convolution Neural Network (FCNN) with Laplace Mechanism for injecting noise. The processes handled here are data processing and query processing. The dataset is uploaded by the data owner to the data provider, who is responsible for injecting noise and then encrypting with Piccolo encryption before uploading it into the cloud. Based on the uploaded dataset, the data owner constructs a hash index from the extracted key attributes by using the BLAKE2s algorithm for performing hashing. The hash index is fed into the edge server to form a Merkle hash tree due to the data leakage at the edge is eliminated. On the other hand, requests/queries by the data analyst are authenticated by the data provider. The hash tree in the edge server then searches for the corresponding data, extracting it from the cloud and delivers it to the data analyst in an encrypted format. Every authenticated data analyst is provided with a decryption key for retrieving the query result. This is implemented using Java and the results show better efficiency in terms of scalability, processing time and accuracy.

show abstract

“…In the digital age, balancing public safety and personal privacy is still enormously challenging [38,39,40]. With the rapid spread of COVID-19, unidentified aggregate information has little value.…”

Section: A Threat Analysis Using the Stride Threat Modelmentioning

confidence: 99%

Balancing Personal Privacy and Public Safety During COVID-19: The Case of South Korea

et al. 2020

View full text Add to dashboard Cite

There has been vigorous debate on how different countries responded to the COVID-19 pandemic. To secure public safety, South Korea actively used personal information at the risk of personal privacy whereas France encouraged voluntary cooperation at the risk of public safety. In this article, after a brief comparison of contextual differences with France, we focus on South Korea's approaches to epidemiological investigations. To evaluate the issues pertaining to personal privacy and public health, we examine the usage patterns of original data, de-identification data, and encrypted data. Our specific proposal discusses the COVID index, which considers collective infection, outbreak intensity, availability of medical infrastructure, and the death rate. Finally, we summarize the findings and lessons for future research and the policy implications.

show abstract

Differential Privacy for Data and Model Publishing of Medical Data

Cited by 42 publications

References 39 publications

Secure and Robust Machine Learning for Healthcare: A Survey

Secure and Robust Machine Learning for Healthcare: A Survey

On Differential Privacy-Based Framework for Enhancing User Data Privacy in Mobile Edge Computing Environment

Balancing Personal Privacy and Public Safety During COVID-19: The Case of South Korea

Contact Info

Product

Resources

About