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

Electronics ◽  
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.

scholarly journals Privacy-Preserving Methods for Feature Engineering Using Blockchain: Review, Evaluation, and Proof of Concept (Preprint)

2019 ◽  
Author(s):  
Michael Jones ◽  
Matthew Johnson ◽  
Mark Shervey ◽  
Joel T Dudley ◽  
Noah Zimmerman
Keyword(s):  
Data Collection ◽  
Data Privacy ◽  
Hybrid Approach ◽  
Privacy Preserving ◽  
Third Party ◽  
Feature Engineering ◽  
Smart Contracts ◽  
Trusted Third Party ◽  
Secure Hardware ◽  
Cryptographic Techniques

BACKGROUND The protection of private data is a key responsibility for research studies that collect identifiable information from study participants. Limiting the scope of data collection and preventing secondary use of the data are effective strategies for managing these risks. An ideal framework for data collection would incorporate feature engineering, a process where secondary features are derived from sensitive raw data in a secure environment without a trusted third party. OBJECTIVE This study aimed to compare current approaches based on how they maintain data privacy and the practicality of their implementations. These approaches include traditional approaches that rely on trusted third parties, and cryptographic, secure hardware, and blockchain-based techniques. METHODS A set of properties were defined for evaluating each approach. A qualitative comparison was presented based on these properties. The evaluation of each approach was framed with a use case of sharing geolocation data for biomedical research. RESULTS We found that approaches that rely on a trusted third party for preserving participant privacy do not provide sufficiently strong guarantees that sensitive data will not be exposed in modern data ecosystems. Cryptographic techniques incorporate strong privacy-preserving paradigms but are appropriate only for select use cases or are currently limited because of computational complexity. Blockchain smart contracts alone are insufficient to provide data privacy because transactional data are public. Trusted execution environments (TEEs) may have hardware vulnerabilities and lack visibility into how data are processed. Hybrid approaches combining blockchain and cryptographic techniques or blockchain and TEEs provide promising frameworks for privacy preservation. For reference, we provide a software implementation where users can privately share features of their geolocation data using the hybrid approach combining blockchain with TEEs as a supplement. CONCLUSIONS Blockchain technology and smart contracts enable the development of new privacy-preserving feature engineering methods by obviating dependence on trusted parties and providing immutable, auditable data processing workflows. The overlap between blockchain and cryptographic techniques or blockchain and secure hardware technologies are promising fields for addressing important data privacy needs. Hybrid blockchain and TEE frameworks currently provide practical tools for implementing experimental privacy-preserving applications.

scholarly journals Privacy-Preserving Methods for Feature Engineering Using Blockchain: Review, Evaluation, and Proof of Concept

10.2196/13600 ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. e13600 ◽  
Author(s):  
Michael Jones ◽  
Matthew Johnson ◽  
Mark Shervey ◽  
Joel T Dudley ◽  
Noah Zimmerman
Keyword(s):  
Data Collection ◽  
Data Privacy ◽  
Hybrid Approach ◽  
Privacy Preserving ◽  
Third Party ◽  
Feature Engineering ◽  
Smart Contracts ◽  
Trusted Third Party ◽  
Secure Hardware ◽  
Cryptographic Techniques

Background The protection of private data is a key responsibility for research studies that collect identifiable information from study participants. Limiting the scope of data collection and preventing secondary use of the data are effective strategies for managing these risks. An ideal framework for data collection would incorporate feature engineering, a process where secondary features are derived from sensitive raw data in a secure environment without a trusted third party. Objective This study aimed to compare current approaches based on how they maintain data privacy and the practicality of their implementations. These approaches include traditional approaches that rely on trusted third parties, and cryptographic, secure hardware, and blockchain-based techniques. Methods A set of properties were defined for evaluating each approach. A qualitative comparison was presented based on these properties. The evaluation of each approach was framed with a use case of sharing geolocation data for biomedical research. Results We found that approaches that rely on a trusted third party for preserving participant privacy do not provide sufficiently strong guarantees that sensitive data will not be exposed in modern data ecosystems. Cryptographic techniques incorporate strong privacy-preserving paradigms but are appropriate only for select use cases or are currently limited because of computational complexity. Blockchain smart contracts alone are insufficient to provide data privacy because transactional data are public. Trusted execution environments (TEEs) may have hardware vulnerabilities and lack visibility into how data are processed. Hybrid approaches combining blockchain and cryptographic techniques or blockchain and TEEs provide promising frameworks for privacy preservation. For reference, we provide a software implementation where users can privately share features of their geolocation data using the hybrid approach combining blockchain with TEEs as a supplement. Conclusions Blockchain technology and smart contracts enable the development of new privacy-preserving feature engineering methods by obviating dependence on trusted parties and providing immutable, auditable data processing workflows. The overlap between blockchain and cryptographic techniques or blockchain and secure hardware technologies are promising fields for addressing important data privacy needs. Hybrid blockchain and TEE frameworks currently provide practical tools for implementing experimental privacy-preserving applications.

scholarly journals Applying Blockchain Technology to Address the Crisis of Trust During the COVID-19 Pandemic

10.2196/20477 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e20477 ◽  
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.

scholarly journals A Novel Privacy-Preserving Scheme in IoT-Based Social Distancing Technologies

2021 ◽  
Author(s):  
Arwa Alrawais ◽  
Fatemah Alharbi ◽  
Moteeb Almoteri ◽  
Sara A Aljwair ◽  
Sara SAljwair
Keyword(s):  
Data Privacy ◽  
Personal Information ◽  
Fog Computing ◽  
Mortality Rates ◽  
Privacy Preserving ◽  
Third Party ◽  
Massive Data ◽  
Social Distancing ◽  
The World ◽  
Efficiency And Effectiveness

The COVID-19 pandemic has swapped the world, causing enormous cases, which led to high mortality rates across the globe. Internet of Things (IoT) based social distancing techniques and many current and emerging technologies have contributed to the fight against the spread of pandemics and reduce the number of positive cases. These technologies generate massive data, which will pose a significant threat to data owners’ privacy by revealing their lifestyle and personal information since that data is stored and managed by a third party like a cloud. This paper provides a new privacy-preserving scheme based on anonymization using an improved slicing technique and implying distributed fog computing. Our implementation shows that the proposed approach ensures data privacy against a third party intending to violate it for any purpose. Furthermore, our results illustrate our scheme’s efficiency and effectiveness.

Cryptographic Primitives in Blockchain Technology

2020 ◽  
Author(s):  
Andreas Bolfing
Keyword(s):  
Third Party ◽  
Quantum Computers ◽  
Cryptographic Primitives ◽  
Signature Schemes ◽  
Trusted Third Party ◽  
Blockchain Technology ◽  
Cryptographic Primitive ◽  
Weak Points ◽  
Potential Risks ◽  
The Impact

Many online applications, especially in the financial industries, are running on blockchain technologies in a decentralized manner, without the use of an authoritative entity or a trusted third party. Such systems are only secured by cryptographic protocols and a consensus mechanism. As blockchain-based solutions will continue to revolutionize online applications in a growing digital market in the future, one needs to identify the principal opportunities and potential risks. Hence, it is unavoidable to learn the mathematical and cryptographic procedures behind blockchain technology in order to understand how such systems work and where the weak points are. The book provides an introduction to the mathematical and cryptographic concepts behind blockchain technologies and shows how they are applied in blockchain-based systems. This includes an introduction to the general blockchain technology approaches that are used to build the so-called immutable ledgers, which are based on cryptographic signature schemes. As future quantum computers will break some of the current cryptographic primitive approaches, the book considers their security and presents the current research results that estimate the impact on blockchain-based systems if some of the cryptographic primitive break. Based on the example of Bitcoin, it shows that weak cryptographic primitives pose a possible danger for the ledger, which can be overcome through the use of the so-called post-quantum cryptographic approaches which are introduced as well.

Privacy Preserving Classification of Biomedical Data With Secure Removing of Duplicate Records

2021 ◽  
pp. 569-588
Author(s):  
Boudheb Tarik ◽  
Elberrichi Zakaria
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Third Party ◽  
Distributed Data ◽  
Biomedical Data ◽  
Collaborative Models ◽  
Highly Sensitive ◽  
Complete Access

Classifying data is to automatically assign predefined classes to data. It is one of the main applications of data mining. Having complete access to all data is critical for building accurate models. Data can be highly sensitive, such as biomedical data, which cannot be disclosed or shared with third party, because it can harm individuals and organizations. The challenge is how to preserve privacy and usefulness of data. Privacy preserving classification addresses this problem. Collaborative models are constructed over networks without violating the data owners' privacy. In this article, the authors address two problems: privacy records deduplication of the same records and privacy-preserving classification. They propose a randomized hash technic for deduplication and an enhanced privacy preserving classification of biomedical data over horizontally distributed data based on two homomorphic encryptions. No private, intermediate or final results are disclosed. Experimentations show that their solution is efficient and secure without loss of accuracy.

Blockchain Technology for Convergence

2021 ◽  
pp. 1-17
Author(s):  
Manpreet Kaur ◽  
Shikha Gupta
Keyword(s):  
Decision Making ◽  
Financial Services ◽  
Data Privacy ◽  
Unified Model ◽  
Third Party ◽  
Comprehensive Overview ◽  
Blockchain Technology ◽  
Disruptive Innovations

Blockchain technologies are drawing attention after the success of cryptocurrency. Due to the inherent features, such as decentralization, transparency, security, immutability, and integrity, they have already become the prime choice of researchers and scientists. Blockchain is among the most disruptive innovations which have the potential to reshape the behavior of many businesses and industries. Blockchain applications are based on DLT in which public ledger can be accessed by everyone by eliminating the need of third party. Although the power of AI allows the intelligence and decision-making powers of machines in the same way as humans, it relies on a unified model for training and validating datasets. However, the unified nature of AI poses many threats to data privacy and data tempering. Thus, the unique features of blockchain technology makes its application attractive in almost every field including financial services, healthcare, IoT, and many more. This chapter presents a comprehensive overview on blockchain and its integration with AI to explore numerous capabilities.

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

Electronics ◽  
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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