Multi-Tier Reputation for Data Cooperatives

Salau, Abiola; Dantu, Ram; Morozov, Kirill; Upadhyay, Kritagya; Badruddoja, Syed

doi:10.1007/978-3-031-18679-0_14

Cited by 8 publications

(6 citation statements)

References 38 publications

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“…Additionally, in discussing PoS, one can see that PoS is not associated with reputation and hence it cannot be directly applied in our scenarios. A natural similarity to these types of systems is seen when directly mapping reputation to stake (as it is done, e.g., in [60,74]). Then, the components such as committee selection and block mining work in a similar manner as in PoS systems.…”

Section: Discussionmentioning

confidence: 99%

“…However, we apply it in a different context of evaluating review credibility. Also, the work [60] uses a different blockchain system, the Snow White protocol.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, the reputation forms a "quasi-stake", which each reviewer has in the system. This approach is the same way as it is done, e.g., in [60]. This way, the reputation is used as an asset/incentive to encourage honest behavior of the participants.…”

Section: Overview Of Our Contributionmentioning

confidence: 99%

“…Proof-of-Stake: A natural similarity to these types of systems is seen when directly mapping reputation to stake (as it is done, e.g., in [60,74]. Then, the components such as committee selection and block mining work in a similar manner as in PoS systems.…”

Section: Proof-of-workmentioning

confidence: 99%

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Using Blockchain to Ensure Reputation Credibility in Decentralized Review Management

Zaccagni

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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.

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Section: Discussionmentioning

confidence: 99%

“…However, we apply it in a different context of evaluating review credibility. Also, the work [60] uses a different blockchain system, the Snow White protocol.…”

Section: Related Workmentioning

confidence: 99%

Section: Overview Of Our Contributionmentioning

confidence: 99%

Section: Proof-of-workmentioning

confidence: 99%

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Using Blockchain to Ensure Reputation Credibility in Decentralized Review Management

Zaccagni

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“…Prediction accuracy of learning smart contracts: Smart contracts [10] of blockchain technology execute functions with static rules to secure transactions that are recorded in the distributed ledger as a result of mining a block [114,164,148,47]. Financial trades, reputation systems, legal contracts , and ownership validation are static transactions that do not require to learn updates or possess cognitive intelligence [158,156,157,155,130,131,132]. Machine learning algorithms require a dynamic update of learning models that can sustain the needs of training models at different times.…”

Section: Expected Performance Evaluationmentioning

confidence: 99%

Blockchain for AI: Smarter Contracts to Secure Artificial Intelligence Algorithms

Badruddoja

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In this dissertation, I investigate the existing smart contract problems that limit cognitive abilities. I use Taylor's serious expansion, polynomial equation, and fraction-based computations to overcome the limitations of calculations in smart contracts. To prove the hypothesis, I use these mathematical models to compute complex operations of naive Bayes, linear regression, decision trees, and neural network algorithms on Ethereum public test networks. The smart contracts achieve 95\% prediction accuracy compared to traditional programming language models, proving the soundness of the numerical derivations. Many non-real-time applications can use our solution for trusted and secure prediction services.

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