scholarly journals A Quantum Blind Multi-Signature Method for the Industrial Blockchain

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1520
Author(s):  
Zhengying Cai ◽  
Shi Liu ◽  
Zhangyi Han ◽  
Rui Wang

Traditional anti-quantum methods and multi-signature technologies to secure the blockchain against quantum attacks will quickly reduce the efficiency and scalability of the industrial blockchain, where the computational resources will experience a polynomial rise with the increasing number of traders. Here, a quantum blind multi-signature method is proposed for the multi-party transaction to provide anti-quantum security. First, the proposed multi-party transaction frame and quantum key distribution in the industrial blockchain are introduced. It integrates a novel quantum blind multi-signature algorithm that is based on the quantum entanglement mechanism, and it is absolutely secure in theory. Second, the anti-quantum multi-signature algorithm is illustrated, where there are four phases, i.e., initialization, signing, verification, and implementation. Third, the security and complexity of the proposed framework are analyzed and compared with related methods in references, and our proposed method is verified to be able to offer good computational performance and blockchain scalability for multi-party transaction. Last, the paper is summarized and future research directions are proposed.

2021 ◽  
Vol 4 ◽  
Author(s):  
Franziska Boenisch

Machine learning (ML) models are applied in an increasing variety of domains. The availability of large amounts of data and computational resources encourages the development of ever more complex and valuable models. These models are considered the intellectual property of the legitimate parties who have trained them, which makes their protection against stealing, illegitimate redistribution, and unauthorized application an urgent need. Digital watermarking presents a strong mechanism for marking model ownership and, thereby, offers protection against those threats. This work presents a taxonomy identifying and analyzing different classes of watermarking schemes for ML models. It introduces a unified threat model to allow structured reasoning on and comparison of the effectiveness of watermarking methods in different scenarios. Furthermore, it systematizes desired security requirements and attacks against ML model watermarking. Based on that framework, representative literature from the field is surveyed to illustrate the taxonomy. Finally, shortcomings and general limitations of existing approaches are discussed, and an outlook on future research directions is given.


2009 ◽  
Vol 26 (1) ◽  
pp. 46-69
Author(s):  
Shamas-Ur-Rehman Toor

Management from Islamic Perspectives (MIP) is an emerging field that has begun to attract scholarly attention. However, the research undertaken so far has been rather fragmented and lack a clear agenda. This paper presents a literature review of the field and the areas of current focus. Although the field has a huge growth potential, I argue that it faces several challenges and problems as it develops further. I outline these potential pitfalls, suggest how to develop MIP as a formal discipline, and explain how to integrate it within real-life business practices. The article closes with a call for research to be conducted in a more organized fashion through an international consortium of researchers as well as recommendations for future research directions.


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