Towards an End-to-End IoT Data Privacy-Preserving Framework Using Blockchain Technology

Background The widespread death and disruption caused by the COVID-19 pandemic has revealed deficiencies of existing institutions regarding the protection of human health and well-being. Both a lack of accurate and timely data and pervasive misinformation are causing increasing harm and growing tension between data privacy and public health concerns. Objective This aim of this paper is to describe how blockchain, with its distributed trust networks and cryptography-based security, can provide solutions to data-related trust problems. Methods Blockchain is being applied in innovative ways that are relevant to the current COVID-19 crisis. We describe examples of the challenges faced by existing technologies to track medical supplies and infected patients and how blockchain technology applications may help in these situations. Results This exploration of existing and potential applications of blockchain technology for medical care shows how the distributed governance structure and privacy-preserving features of blockchain can be used to create “trustless” systems that can help resolve the tension between maintaining privacy and addressing public health needs in the fight against COVID-19. Conclusions Blockchain relies on a distributed, robust, secure, privacy-preserving, and immutable record framework that can positively transform the nature of trust, value sharing, and transactions. A nationally coordinated effort to explore blockchain to address the deficiencies of existing systems and a partnership of academia, researchers, business, and industry are suggested to expedite the adoption of blockchain in health care.

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Trust Hardware Based Secured Privacy Preserving Computation System for Three-Dimensional Data

Electronics ◽

10.3390/electronics10131546 ◽

2021 ◽

Vol 10 (13) ◽

pp. 1546

Author(s):

Munan Yuan ◽

Xiaofeng Li ◽

Xiru Li ◽

Haibo Tan ◽

Jinlin Xu

Keyword(s):

Data Privacy ◽

High Performance ◽

Distributed Storage ◽

Three Dimensional ◽

Privacy Preserving ◽

Sensitive Data ◽

Privacy And Security ◽

Time Consumption ◽

Blockchain Technology ◽

3D Data

Three-dimensional (3D) data are easily collected in an unconscious way and are sensitive to lead biological characteristics exposure. Privacy and ownership have become important disputed issues for the 3D data application field. In this paper, we design a privacy-preserving computation system (SPPCS) for sensitive data protection, based on distributed storage, trusted execution environment (TEE) and blockchain technology. The SPPCS separates a storage and analysis calculation from consensus to build a hierarchical computation architecture. Based on a similarity computation of graph structures, the SPPCS finds data requirement matching lists to avoid invalid transactions. With TEE technology, the SPPCS implements a dual hybrid isolation model to restrict access to raw data and obscure the connections among transaction parties. To validate confidential performance, we implement a prototype of SPPCS with Ethereum and Intel Software Guard Extensions (SGX). The evaluation results derived from test datasets show that (1) the enhanced security and increased time consumption (490 ms in this paper) of multiple SGX nodes need to be balanced; (2) for a single SGX node to enhance data security and preserve privacy, an increased time consumption of about 260 ms is acceptable; (3) the transaction relationship cannot be inferred from records on-chain. The proposed SPPCS implements data privacy and security protection with high performance.

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Applying Blockchain Technology to Address the Crisis of Trust During the COVID-19 Pandemic (Preprint)

10.2196/preprints.20477 ◽

2020 ◽

Cited By ~ 3

Author(s):

Anjum Khurshid

Keyword(s):

Public Health ◽

Data Privacy ◽

Well Being ◽

Governance Structure ◽

Privacy Preserving ◽

Trust Value ◽

Blockchain Technology ◽

Trust Networks ◽

Technology Applications ◽

Potential Applications

BACKGROUND The widespread death and disruption caused by the COVID-19 pandemic has revealed deficiencies of existing institutions regarding the protection of human health and well-being. Both a lack of accurate and timely data and pervasive misinformation are causing increasing harm and growing tension between data privacy and public health concerns. OBJECTIVE This aim of this paper is to describe how blockchain, with its distributed trust networks and cryptography-based security, can provide solutions to data-related trust problems. METHODS Blockchain is being applied in innovative ways that are relevant to the current COVID-19 crisis. We describe examples of the challenges faced by existing technologies to track medical supplies and infected patients and how blockchain technology applications may help in these situations. RESULTS This exploration of existing and potential applications of blockchain technology for medical care shows how the distributed governance structure and privacy-preserving features of blockchain can be used to create “trustless” systems that can help resolve the tension between maintaining privacy and addressing public health needs in the fight against COVID-19. CONCLUSIONS Blockchain relies on a distributed, robust, secure, privacy-preserving, and immutable record framework that can positively transform the nature of trust, value sharing, and transactions. A nationally coordinated effort to explore blockchain to address the deficiencies of existing systems and a partnership of academia, researchers, business, and industry are suggested to expedite the adoption of blockchain in health care.

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Privacy-Preserving K-Nearest Neighbors Training over Blockchain-Based Encrypted Health Data

Electronics ◽

10.3390/electronics9122096 ◽

2020 ◽

Vol 9 (12) ◽

pp. 2096

Author(s):

Rakib Ul Haque ◽

A S M Touhidul Hasan ◽

Qingshan Jiang ◽

Qiang Qu

Keyword(s):

Data Privacy ◽

Building Blocks ◽

Privacy Preserving ◽

Training Data ◽

Third Party ◽

Supervised Machine Learning ◽

Primary Concern ◽

Privacy Issue ◽

Trusted Third Party ◽

Blockchain Technology

Numerous works focus on the data privacy issue of the Internet of Things (IoT) when training a supervised Machine Learning (ML) classifier. Most of the existing solutions assume that the classifier’s training data can be obtained securely from different IoT data providers. The primary concern is data privacy when training a K-Nearest Neighbour (K-NN) classifier with IoT data from various entities. This paper proposes secure K-NN, which provides a privacy-preserving K-NN training over IoT data. It employs Blockchain technology with a partial homomorphic cryptosystem (PHC) known as Paillier in order to protect all participants (i.e., IoT data analyst C and IoT data provider P) data privacy. When C analyzes the IoT data of P, both participants’ privacy issue arises and requires a trusted third party. To protect each candidate’s privacy and remove the dependency on a third-party, we assemble secure building blocks in secure K-NN based on Blockchain technology. Firstly, a protected data-sharing platform is developed among various P, where encrypted IoT data is registered on a shared ledger. Secondly, the secure polynomial operation (SPO), secure biasing operations (SBO), and secure comparison (SC) are designed using the homomorphic property of Paillier. It shows that secure K-NN does not need any trusted third-party at the time of interaction, and rigorous security analysis demonstrates that secure K-NN protects sensitive data privacy for each P and C. The secure K-NN achieved 97.84%, 82.33%, and 76.33% precisions on BCWD, HDD, and DD datasets. The performance of secure K-NN is precisely similar to the general K-NN and outperforms all the previous state of art methods.

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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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End-to-end privacy preserving scheme for IoT-based healthcare systems

Wireless Networks ◽

10.1007/s11276-021-02652-9 ◽

2021 ◽

Author(s):

Maryam Nasr Esfahani ◽

Behrouz Shahgholi Ghahfarokhi ◽

Shahram Etemadi Borujeni

Keyword(s):

Healthcare Systems ◽

Privacy Preserving ◽

End To End

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Deep learning based Blockchain solution for preserving privacy in future vehicles

International Journal of Hybrid Intelligent Systems ◽

10.3233/his-200289 ◽

2020 ◽

pp. 1-14

Author(s):

Varsha R ◽

Meghna Manoj Nair ◽

Siddharth M. Nair ◽

Amit Kumar Tyagi

Keyword(s):

Deep Learning ◽

Autonomous Vehicles ◽

Recommendation System ◽

Privacy Preserving ◽

Smart Devices ◽

The Road ◽

Blockchain Technology ◽

Technological Advances ◽

Previous Decade ◽

Smart Things

The Internet of Things (smart things) is used in many sectors and applications due to recent technological advances. One of such application is in the transportation system, which is of primary use for the users to move from one place to another place. The smart devices which were embedded in vehicles are useful for the passengers to solve his/her query, wherein future vehicles will be fully automated to the advanced stage, i.e. future cars with driverless feature. These autonomous cars will help people a lot to reduce their time and increases their productivity in their respective (associated) business. In today’s generation and in the near future, privacy preserving and trust will be a major concern among users and autonomous vehicles and hence, this paper will be able to provide clarity for the same. Many attempts in previous decade have provided many efficient mechanisms, but they all work only with vehicles along with a driver. However, these mechanisms are not valid and useful for future vehicles. In this paper, we will use deep learning techniques for building trust using recommender systems and Blockchain technology for privacy preserving. We also maintain a certain level of trust via maintaining the highest level of privacy among users living in a particular environment. In this research, we developed a framework that could offer maximum trust or reliable communication to users over the road network. With this, we also preserve privacy of users during traveling, i.e., without revealing identity of respective users from Trusted Third Parties or even Location Based Service in reaching a destination. Thus, Deep Learning based Blockchain Solution (DLBS) is illustrated for providing an efficient recommendation system.

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End-to-end privacy preserving deep learning on multi-institutional medical imaging

Nature Machine Intelligence ◽

10.1038/s42256-021-00337-8 ◽

2021 ◽

Author(s):

Georgios Kaissis ◽

Alexander Ziller ◽

Jonathan Passerat-Palmbach ◽

Théo Ryffel ◽

Dmitrii Usynin ◽

...

Keyword(s):

Deep Learning ◽

Medical Imaging ◽

Privacy Preserving ◽

End To End

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Privacy-Preserving Sorting Algorithms Based on Logistic Map for Clouds

Security and Communication Networks ◽

10.1155/2018/2373545 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10

Author(s):

Hua Dai ◽

Hui Ren ◽

Zhiye Chen ◽

Geng Yang ◽

Xun Yi

Keyword(s):

Data Privacy ◽

Logistic Map ◽

Security Analysis ◽

Privacy Preserving ◽

Service Recommendation ◽

Sensitive Data ◽

Encrypted Data ◽

Sorting Algorithms ◽

Common Operation ◽

Cloud Servers

Outsourcing data in clouds is adopted by more and more companies and individuals due to the profits from data sharing and parallel, elastic, and on-demand computing. However, it forces data owners to lose control of their own data, which causes privacy-preserving problems on sensitive data. Sorting is a common operation in many areas, such as machine learning, service recommendation, and data query. It is a challenge to implement privacy-preserving sorting over encrypted data without leaking privacy of sensitive data. In this paper, we propose privacy-preserving sorting algorithms which are on the basis of the logistic map. Secure comparable codes are constructed by logistic map functions, which can be utilized to compare the corresponding encrypted data items even without knowing their plaintext values. Data owners firstly encrypt their data and generate the corresponding comparable codes and then outsource them to clouds. Cloud servers are capable of sorting the outsourced encrypted data in accordance with their corresponding comparable codes by the proposed privacy-preserving sorting algorithms. Security analysis and experimental results show that the proposed algorithms can protect data privacy, while providing efficient sorting on encrypted data.

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Blockchain for healthcare records: A data perspective

10.7287/peerj.preprints.26942 ◽

2018 ◽

Author(s):

Mian Zhang ◽

Yuhong Ji

Keyword(s):

Data Privacy ◽

Healthcare Systems ◽

Multiple Perspectives ◽

Health Records ◽

Healthcare Data ◽

Blockchain Technology ◽

Security Control ◽

Patient Health ◽

And Storage ◽

Insight Into

A problem facing healthcare record systems throughout the world is how to share the medical data with more stakeholders for various purposes without sacrificing data privacy and integrity. Blockchain, operating in a state of consensus, is the underpinning technology that maintains the Bitcoin transaction ledger. Blockchain as a promising technology to manage the transactions has been gaining popularity in the domain of healthcare. Blockchain technology has the potential of securely, privately, and comprehensively manage patient health records. In this work, we discuss the latest status of blockchain technology and how it could solve the current issues in healthcare systems. We evaluate the blockchain technology from the multiple perspectives around healthcare data, including privacy, security, control, and storage. We review the current projects and researches of blockchain in the domain of healthcare records and provide the insight into the design and construction of next generations of blockchain-based healthcare systems.

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