Abstract
Verifiable Machine Learning can play a key role in both extending and enhancing functionalities of social graphs through the extrapolation of trust levels, anomaly detection, and integrity assurance . While industries such as social media, finance, and e-commerce widely utilize these approaches, analogous capabilities in Web3 are scarce. As part of a research partnership with CirclesUBI, we at Giza explore the feasibility of meaningful data analysis, model training , and trust score generation.
Here, we introduce the concept of Universal Basic Income (UBI) and the Circles protocol, the challenges in building a robust trust network, and how machine learning can help. We discuss Giza's model development based on the Circles Database, review the methodologies employed for data preprocessing, and wrap up with insights and final observations from this technical analysis.
Introduction
UBI is a long-standing socio-economic policy framework that guarantees every citizen a regular, unconditional stipend irrespective of employment, income, or social standing. Circles aims to be the leading blockchain-native UBI infrastructure, whereby each participant is endowed with a unique, algorithmically minted personal currency connected through a trust-based social graph, namely a Web-of-Trust. Although WoT can be an effective peer-to-peer trust management system for a non-bureaucratic decentralized identity framework, it has its inherent weaknesses. These include challenges around establishment of initial trust, decay of extant trust relationships, and Sybil attack risks.
In order to address these, Giza and Circles are researching ways in which verifiable machine learning–in particular algorithmic trust scoring –can enhance the UBI network’s resilience against Sybil threats. This report is the first analytical output from this collaboration, highlighting initial observations derived from data-driven explorations.
Problem
With the objective to identify potentially malicious agents and fake accounts in the Circles Network using machine learning, Giza has conducted a network-based data analysis. The project consists of a relatively conventional order of preliminary processes, with data collection and initial exploration followed by data cleaning, preprocessing, and feature extraction.
Solution
Verifiable Machine Learning can play a key role in both extending and enhancing functionalities of social graphs through the extrapolation of trust levels, anomaly detection, and integrity assurance . While industries such as social media, finance, and e-commerce widely utilize these approaches, analogous capabilities in Web3 are scarce. As part of a research partnership with CirclesUBI, we at Giza explore the feasibility of meaningful data analysis, model training , and trust score generation.
Here, we introduce the concept of Universal Basic Income (UBI) and the Circles protocol, the challenges in building a robust trust network, and how machine learning can help. We discuss Giza's model development based on the Circles Database, review the methodologies employed for data preprocessing, and wrap up with insights and final observations from this technical analysis.
Giza Architecture
UBI is a long-standing socio-economic policy framework that guarantees every citizen a regular, unconditional stipend irrespective of employment, income, or social standing. Circles aims to be the leading blockchain-native UBI infrastructure, whereby each participant is endowed with a unique, algorithmically minted personal currency connected through a trust-based social graph, namely a Web-of-Trust. Although WoT can be an effective peer-to-peer trust management system for a non-bureaucratic decentralized identity framework, it has its inherent weaknesses. These include challenges around establishment of initial trust, decay of extant trust relationships, and Sybil attack risks.
In order to address these, Giza and Circles are researching ways in which verifiable machine learning–in particular algorithmic trust scoring –can enhance the UBI network’s resilience against Sybil threats. This report is the first analytical output from this collaboration, highlighting initial observations derived from data-driven explorations.
Use Cases
With the objective to identify potentially malicious agents and fake accounts in the Circles Network using machine learning, Giza has conducted a network-based data analysis. The project consists of a relatively conventional order of preliminary processes, with data collection and initial exploration followed by data cleaning, preprocessing, and feature extraction.
Conclusion
With the objective to identify potentially malicious agents and fake accounts in the Circles Network using machine learning, Giza has conducted a network-based data analysis. The project consists of a relatively conventional order of preliminary processes, with data collection and initial exploration followed by data cleaning, preprocessing, and feature extraction.
