Blockchain-Based Reputation Systems: Implementation Challenges and Mitigation
- Survey current research difficulties in DLT-based RMSs and its enabling technologies. Highlighting some of the common pitfalls in designing and implementing a reputation system that is based on DLT.
- Provide insight into the challenges that are faced with recommendations and solutions to avoid the respective pitfalls. We discuss in detail the nature of the difficulties of merging a DLT with a reputation system.
- Analyze the effect of utilizing a fraction of service feedbacks to provide an accurate reputation with minimal feedbacks. We produce simulations that show that truncating the available feedbacks to a certain level will still maintain the reputation level. This is possible by taking the temporal behavior change into account.
2. On-Chain and Off-Chain Tradeoffs
2.1. Consensus Computation
2.2. Data Management
2.3. Integrity and Reliability
2.4. The Case of Reputation Systems
3. External Interaction Issues
3.1. Enabling External Interaction
3.2. Establishing Trust
4. The Requirement of Deterministic Results
4.1. The Rationale of Determinism
4.2. The Impact on Smart Contract Operations
5. Time Management in the Context of Smart Contracts
Temporal Adaptability in Reputation
- The equation is recursive and does not require the aggregation of past feedbacks to generate the new reputation, but it is computed only while using the currently provided feedback.
- Time is explicitly incorporated by utilizing the smart contract supported Unix Epoch clock.
- A simple linear equation is used as opposed to a complex non-linear equation, in order to accommodate for the smart contract computation capabilities in a practical manner.
6. The Impact on Reputation Update
6.1. Proactive Updates
6.2. Reactive Updates
7. Reducing Smart Contract Storage Requirements
- Scenario 1: the goal of this scenario is to assess the impact of the number of considered feedbacks on the reputation value. In this scenario, we consider the feedbacks regarding a specific product. The number of total feedbacks is set to the following values . From the 10,000 feedbacks, the 100 and 1000 feedbacks are chosen as the oldest 100 and 1000 feedbacks, respectively. These may represent the evolution of the feedbacks that are received over time. The reputation values are computed while using Equation (4). Equation (6) is used in order to compute the percentage error incurred if we only consider a specific number of recent feedbacks (i.e., truncated reputation) out of the total number of feedbacks (i.e., original reputation). The weights of Equation (4) were held constant at .
- Scenario 2: the goal of this scenario is to assess the impact of the weights on the reputation value. The number of considered feedbacks is set to 10,000. The considered weight values are .
- Scenario 3: the goal of this scenario is to confirm that the behavior of the error with respect to the number of considered feedbacks is the same for any product. Three instant videos were randomly selected from the Amazon dataset. The total number of feedbacks was set to 10,000 as in the previous scenario, and the weight was similar to Scenario 1, being at .
Conflicts of Interest
|BFT||Byzantine Fault Tolerance|
|DLT||Distributed Ledger Technology|
|RMS||Reputation Management System|
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|Gas Cost||Ether||USD ($)||Storage Size (Bytes)|
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Battah, A.; Iraqi, Y.; Damiani, E. Blockchain-Based Reputation Systems: Implementation Challenges and Mitigation. Electronics 2021, 10, 289. https://doi.org/10.3390/electronics10030289
Battah A, Iraqi Y, Damiani E. Blockchain-Based Reputation Systems: Implementation Challenges and Mitigation. Electronics. 2021; 10(3):289. https://doi.org/10.3390/electronics10030289Chicago/Turabian Style
Battah, Ammar, Youssef Iraqi, and Ernesto Damiani. 2021. "Blockchain-Based Reputation Systems: Implementation Challenges and Mitigation" Electronics 10, no. 3: 289. https://doi.org/10.3390/electronics10030289