Reputation Systems for Supply Chains: The Challenge of Achieving Privacy Preservation

Consumers frequently interact with reputation systems to rate products, services, and deliveries. While past research extensively studied different conceptual approaches to realize such systems securely and privacy-preservingly, these concepts are not yet in use in business-to-business environments. In this paper, (1) we thus outline which specific challenges privacy-cautious stakeholders in volatile supply chain networks introduce, (2) give an overview of the diverse landscape of privacy-preserving reputation systems and their properties, and (3) based on well-established concepts from supply chain information systems and cryptography, we further propose an initial concept that accounts for the aforementioned challenges by utilizing fully homomorphic encryption. For future work, we identify the need of evaluating whether novel systems address the supply chain-specific privacy and confidentiality needs

Trust Development in Artificial Intelligence-based Emerging Technologies: Rise of Technomoral Virtues and Data Ethics

Ethical usage of artificial intelligence and data science is a rapidly evolving topic of discussion among individuals, organizations, and society. More attention has been paid to moral rules and regulations during such discussions than these stakeholders’ moral character development. This study examines how individuals deploy their moral decision-making skills under conditions of uncertainty. What virtues are the most important or most unimportant virtues in their decision to develop trust in artificial intelligence-based emerging technologies in the presence of personal information privacy threats? Using Q-methodology, the Concourse theory, and virtue ethics, four viewpoints (i.e., virtues-based decision-making structures) of individuals are extracted from a group of 39 participants for developing trust in emerging technologies. The findings of this study are of interest to philosophers, ethicists, and other stakeholders who work in the areas of moral decision-making under uncertainty, artificial intelligence, and data ethics

Using Blockchain to Ensure Reputation Credibility in Decentralized Review Management

In recent years, there have been incidents which decreased people's trust in some organizations and authorities responsible for ratings and accreditation. For a few prominent examples, there was a security breach at Equifax (2017), misconduct was found in the Standard & Poor's Ratings Services (2015), and the Accrediting Council for Independent Colleges and Schools (2022) validated some of the low-performing schools as delivering higher standards than they actually were. A natural solution to these types of issues is to decentralize the relevant trust management processes using blockchain technologies. The research problems which are tackled in this thesis consider the issue of trust in reputation for assessment and review credibility at different angles, in the context of blockchain applications. We first explored the following questions. How can we trust courses in one college to provide students with the type and level of knowledge which is needed in a specific workplace? Micro-accreditation on a blockchain was our solution, including using a peer-review system to determine the rigor of a course (through a consensus). Rigor is the level of difficulty in regard to a student's expected level of knowledge. Currently, we make assumptions about the quality and rigor of what is learned, but this is prone to human bias and misunderstandings. We present a decentralized approach that tracks student records throughout the academic progress at a school and helps to match employers' requirements to students' knowledge. We do this by applying micro-accredited topics and Knowledge Units (KU) defined by NSA's Center of Academic Excellence to courses and assignments. We demonstrate that the system was successful in increasing accuracy of hires through simulated datasets, and that it is efficient, as well as scalable. Another problem is how can we trust that the peer reviews are honest and reflect an accurate rigor score? Assigning reputation to peers is a natural method to ensure correctness of these assessments. The reputation of the peers providing rigor scores needs to be taken into account for an overall rigor of a course, its topics, and its tasks. Specifically, those with a higher reputation should have more influence on the total score. Hence, we focused on how a peer's reputation is managed. We explored decentralized reputation management for the peers, choosing a decentralized marketplace as a sample application. We presented an approach to ensuring review credibility, which is a particular aspect of trust in reviews and reputation of the parties who provide them. We use a Proof-of-Stake based Algorand system as a base of our implementation, since this system is open-source, and it has a rich community support. Specifically, we directly map reputation to stake, which allows us to deploy Algorand at the blockchain layer. Reviews are analyzed by the proposed evaluation component using Natural Language Processing (NLP). In our system, NLP gauges the positivity of the written review, compares that value to a scaled numerical rating given, and determines adjustments to a peer's reputation from that result. We demonstrate that this architecture ensures credible and trustworthy assessments. It also efficiently manages the reputation of the peers, while keeping reasonable consensus times. We then turned our focus on ensuring that a peer's reputation is credible. This led us to introducing a new type of consensus called "Proof-of-Review". Our proposed implementation is again based on Algorand, since its modular architecture allows for easy modifications, such as adding extra components, but this time, we modified the engine. The proposed model then provides a trust in evaluations (review and assessment credibility) and in those who provide them (reputation credibility) using a blockchain. We introduce a blacklisting component, which prevents malicious nodes from participating in the protocol, and a minimum-reputation component, which limits the influence of under-performing users. Our results showed that the proposed blockchain system maintains liveliness and completeness. Specifically, blacklisting and the minimum-reputation requirement (when properly tuned) do not affect these properties. We note that the Proof-of-Review concept can be deployed in other types of applications with similar needs of trust in assessments and the players providing them, such as sensor arrays, autonomous car groups (caravans), marketplaces, and more

Novel artificial intelligence method for decision chain within blockchain technology

The objective of the distributed system is to distribute the resources and the calculations. Blockchain is the art of interconnecting data into a tamper-proof and tamper-resistant ledger. Security is ensured by making the cost of malicious activities very high, trans- parency is inherited from a high level of duplication, and privacy is the result of using cryptography. Consensus is at the heart of the technology to orchestrate nodes to provide finality. However, it has a disadvantage because it bases the decision on different means, which are votes, stake or resources. The decision makes the system prone to monopoly or inconsistencies. In addition, the system suffers from a high validation lag compared to centralized systems. Thus, the injection of a novel artificial intelligence method that can learn and automate the space of actions allow the technology to respond to criticisms of efficiency. This work introduces a new approach in the maintenance of distributed ledger. It will start with the introduction of TheChain as a platform, which is based on the concept of node independence as incentive for competency. Second, TheCoin is the data that will be exchanged between different nodes, which is flexibly modeled to hold different types of symbolic elements. Finally, TheTree is a sociology-inspired approach to maintain va- lidity. It introduced the concept model as a distributed modeling approach and changed decision and security from a component to a network. At TheChain level, monopoly as a philosophical issue was addressed, a conceptual comparison was demonstrated, a se- curity discussion and an operation scenario were investigated. At TheCoin level, discus- sion of security, conceptual comparison, system size and performance are demonstrated. TheTree section will provide a safety discussion, formal study, environment modelisation and conceptual comparisons. The contribution is to provide a non-monopoly-prone plat- form built on a new philosophical principle to solve security problems. Second, TheCoin reduce the size of the block and retain the use of coins to offer parallel transaction pro- cessing, in which it has been reported that TheCoin can be with 10% of normal block size in case of micropayment. TheTree defined a new approach to dealing with malicious users by leveraging regional consistency. The propagation and consistency times are faster than any previous work. Moreover, the cost of malicious activities has been shown to be very high

Artificial intelligence in education

The article is an excerpt from Wayne Holmes/ Maya Bialik/ Charles Fadel, Artificial Intelligence in Education : Promises and Implications for Teaching and Learning, The Center for Curriculum Redesign, Boston, 2019, 151-180 (ISBN-13: 978-1-794-29370-0). Abstract available from: https://discovery.ucl.ac.uk/id/eprint/10139722/). Reprinted with permission of the publisher

Artificial intelligence in education

The article is an excerpt from Wayne Holmes/ Maya Bialik/ Charles Fadel, Artificial Intelligence in Education : Promises and Implications for Teaching and Learning, The Center for Curriculum Redesign, Boston, 2019, 151-180 (ISBN-13: 978-1-794-29370-0). Abstract available from: https://discovery.ucl.ac.uk/id/eprint/10139722/).Reprinted with permission of the publisher

PLEDGE: An IoT-oriented Proof-of-Honesty based Blockchain Consensus Protocol

The existing lottery-based consensus algorithms, such as Proof-of-Work, and Proof-of-Stake, are mostly used for blockchain-based financial technology applications. Similarly, the Byzantine Fault Tolerance algorithms do provide consensus finality, yet they are either communications intensive, vulnerable to Denial-of-Service attacks, poorly scalable, or have a low faulty node tolerance level. Moreover, these algorithms are not designed for the Internet of Things systems that require near-real-time transaction confirmation, maximum fault tolerance, and appropriate transaction validation rules. Hence, we propose "Pledge, "a unique Proof-of-Honesty based consensus protocol to reduce the possibility of malicious behavior during blockchain consensus. Pledge also introduces the Internet of Things centric transaction validation rules. Initial experimentation shows that Pledge is economical and secure with low communications complexity and low latency in transaction confirmation