Machine learning based privacy-preserving fair data trading in big data market

Zhao, Yanqi; Yu, Yong; Li, Yannan; Han, Gang; Du, Xiaojiang

doi:10.1016/j.ins.2018.11.028

Cited by 110 publications

(47 citation statements)

References 7 publications

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“…For example, DLT-based data markets provide the ability to create economic incentives, which could not only stimulate the democratization of access to extant, high-quality AI training data (i.e., addressing the training data availability tension) but as well encourage greater participation by the general public to drive the generation of new, more diverse data sets (i.e., addressing the training data bias tension). However, despite first technical solutions being developed by researchers from the IS, computer science, and related disciplines (Ozercan et al 2018;Özyilmaz et al 2018;Xiong and Xiong 2019;Zhao et al 2019), the question of how to effectively design token economies (e.g., to democratize data access or to encourage the generation of more diverse data sets) remains a focal theme of contemporary DLT research. Adding to this, several researchers have raised concerns over the potential consequences of over-emphasizing economic incentives for the sharing of personal data because they could especially motivate those in need to share their data and without making Table 4 Fruitful avenues of future research on the DLT-based realization of TAI, related tensions, and exemplary research questions…”

Section: Dlt-based Data Marketsmentioning

confidence: 99%

Trustworthy artificial intelligence

2020

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Artificial intelligence (AI) brings forth many opportunities to contribute to the wellbeing of individuals and the advancement of economies and societies, but also a variety of novel ethical, legal, social, and technological challenges. Trustworthy AI (TAI) bases on the idea that trust builds the foundation of societies, economies, and sustainable development, and that individuals, organizations, and societies will therefore only ever be able to realize the full potential of AI, if trust can be established in its development, deployment, and use. With this article we aim to introduce the concept of TAI and its five foundational principles (1) beneficence, (2) non-maleficence, (3) autonomy, (4) justice, and (5) explicability. We further draw on these five principles to develop a data-driven research framework for TAI and demonstrate its utility by delineating fruitful avenues for future research, particularly with regard to the distributed ledger technology-based realization of TAI.

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Section: Dlt-based Data Marketsmentioning

confidence: 99%

Trustworthy artificial intelligence

2020

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“…In their framework, security procedures for the domain and sensor were used to support both integrity and authentication. Moreover, many researchers [26][27][28][29][30] have pointed out that ECDSA is particularly appropriate for authentication and authorization schemes because it performs lightweight processes during security procedures. Many recent studies [31][32][33][34][35] have also pointed out that SHA1 suffers from collision, preimage, and second preimage attacks.…”

Section: Related Existing Researchmentioning

confidence: 99%

“…Thus, REISCH provides longer network lifetime than the schemes in [78][79][80][81]. Recent research (e.g., [26][27][28][29][30]) has used different ways to improve ECDSA's procedures. However, REISCH provides better performance in terms of ECDSA's signature and verification than existing schemes (as shown in Table 6).…”

Section: Performance Comparisonmentioning

confidence: 99%

REISCH: Incorporating Lightweight and Reliable Algorithms into Healthcare Applications of WSNs

Al-Zubaidie

Zhang

2020

Applied Sciences

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Healthcare institutions require advanced technology to collect patients’ data accurately and continuously. The tradition technologies still suffer from two problems: performance and security efficiency. The existing research has serious drawbacks when using public-key mechanisms such as digital signature algorithms. In this paper, we propose Reliable and Efficient Integrity Scheme for Data Collection in HWSN (REISCH) to alleviate these problems by using secure and lightweight signature algorithms. The results of the performance analysis indicate that our scheme provides high efficiency in data integration between sensors and server (saves more than 24% of alive sensors compared to traditional algorithms). Additionally, we use Automated Validation of Internet Security Protocols and Applications (AVISPA) to validate the security procedures in our scheme. Security analysis results confirm that REISCH is safe against some well-known attacks.

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“…The another one provides access control and interoperability using smart contracts and advanced cryptographic primitives. A machine learning based privacy framework for blockchain has been identified recently [34]. It uses fair data trading protocol in big data market and implements its privacy-security features with ring signature, double-authentication-preventing signature and similarity learning.…”

Section: B Recent Application Developments In Blockchain Technologymentioning

confidence: 99%

A Privacy Preserving Distributed Ledger Framework for Global Human Resource Record Management: The Blockchain Aspect

et al. 2020

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Blockchain is a technology used with the series of users in peer-to-peer transactions to utilize the usability properties of the immutable data records. The distributed nature of this technology has given the wide acceptance to its range of applications in various sectors. Seeing the prospect of this new technology, we have chosen the field of human resource management as these data needs to be privacy preserving and confidential along with significant research value. Distributed ledger approach is a novel idea in this field of work specifically for the application of human resource records management. We have used a privacy preserving framework that provides a transparent system for human resource record management. Wallets are generated with organization id and outputting with public-private key pair along with privacy parameter mapping with hash. Keys are used to provide confidentiality, integrity and authentication. Smart contract uses distributed but converged decision with privacy level classification. Performance of the proposed work has been measured based on time, memory consumptions, failure point identification and read-write latencies. The analysis of the results confirms the efficiency of the system.

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Machine learning based privacy-preserving fair data trading in big data market

Cited by 110 publications

References 7 publications

Trustworthy artificial intelligence

Trustworthy artificial intelligence

REISCH: Incorporating Lightweight and Reliable Algorithms into Healthcare Applications of WSNs

A Privacy Preserving Distributed Ledger Framework for Global Human Resource Record Management: The Blockchain Aspect

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