Risk-based ZK-ID for a Better User Experience
KYC, or Know Your Customer, is a marketing process designed to track and verify the identity of individuals engaging in financial transactions, ostensibly to prevent fraud and money laundering. However, the very nature of KYC makes it inherently exploitable. The vast amounts of sensitive personal information collected during the KYC process can become targets for hackers and identity thieves. Moreover, KYC data has been found for sale on dark web markets, indicating a significant risk of exploitation. Additionally, the rigid and standardized nature of KYC procedures can create loopholes that criminals exploit to bypass verification processes. As a result, despite its intentions, KYC poses a substantial risk to individuals' privacy and security, leaving them vulnerable to exploitation by malicious actors.
A Risk-Based Zero Knowledge Identity (RiZKI) is needed to address the vulnerabilities and shortcomings of the traditional KYC process. By adopting a risk-based approach, RiZKI focuses on evaluating the level of risk associated with each user, rather than blindly collecting vast amounts of personal information. This allows for a more tailored and targeted verification process, reducing the amount of sensitive data that needs to be stored and minimizing the risk of exploitation. Additionally, by incorporating Zero Knowledge Proof (ZKP) technology, we can ensure that users can verify their identity without revealing unnecessary personal information, further updating privacy and security. Overall, this concept may offer a more efficient, secure, and privacy-respecting alternative against traditional KYC methods, making it a possible solution or step for addressing the bugs of the current identification process.
Identity in Decentralized Finance for Anonymous Peer-to-Peer Crypto-Fiat Trading
in the fast-paced vers of decentralized finance (DeFi) and peer-to-peer (P2P) trading, customer identification (KYC) has traditionally been a significant hurdle. However, in an effort to enhance user experience and ensure security without compromising privacy, this proposal emerges: Risk-Based Zero Knowledge Identity (RiZKI).
Wadafak is Risk-Based Zero Knowledge Identity?
is an application proposal seeking to replace and improve the traditional KYC method in DeFi and P2P trading. By merging Zero Knowledge Proofs (ZKP — zkSTARKs♠zkSNARKS) technology and a risk-based approach, it aims to ensure a seamless and lightweight user experience in terms of personal information burden, while still providing verified and validated identification.
How does RiZKI work?
by using Automated Contracts, AI and ZKP tech we can allow users to verify their identity without disclosing sensitive personal information to third parties. Instead of requiring an exhaustive list of documents and identity proofs, RiZKI assesses the risk associated with each user and adapts the verification process accordingly.
defining risk involves evaluating a range of factors to assess the likelihood of fraudulent or malicious behavior. This begins with behavioral analysis, where AI algorithms scrutinize transaction patterns, login behaviors, and other usage anomalies. For instance, sudden large transactions or frequent access from different geographic locations can indicate potential risks. Additionally, historical data such as past fraud incidents and reputation scores are considered to gauge the user’s trustworthiness over time.
contextual information plays a crucial role in risk assessment. Factors like geolocation are analyzed to determine the security environment of the user’s access. Users from regions with high fraud rates might be flagged as higher risk. The completeness and accuracy of the user’s profile, including their verification history and network connections, also contribute to the risk evaluation. Cross-referencing user data against external sources like blacklists, watchlists, and credit scores further refines the risk assessment process.
to synthesize these diverse data points, open source machine learning models may be employed. These models detect anomalies and identify patterns that suggest fraudulent behavior, dynamically calculating a comprehensive risk score. This risk score dictates the level of verification required, ensuring that high-risk users undergo more stringent checks while low-risk users benefit from smoother, quicker and higher transactions.
How the Verification Process works
Step 1: Initial Assessment and Risk Scoring
when a user initiates the verification process, AI algorithms first perform a risk assessment. This involves analyzing various factors such as transaction patterns, login behavior, geolocation, and historical data. The AI models detect anomalies and patterns indicative of fraudulent behavior, assigning a comprehensive risk score to the user. This score determines the stringency of the verification process required for that user.
Step 2: Identity Verification Using ZKP
based on the risk score, the system adapts the verification process. Users with lower risk scores undergo a streamlined verification process, while those with higher scores face more rigorous checks. Central to this verification is Zero Knowledge Proof technology. Instead of requiring users to submit extensive personal documents, ZKP allows them to prove their identity or specific attributes (such as age or citizenship) without revealing the actual data. For example, a user can prove they are over 18 without disclosing their exact birthdate.
Step 3: ID Verification and Burning
for users required to verify their identity documents, RiZKI integrates a secure process to verify and “burn” these IDs. When a user submits an ID, the system verifies its authenticity using AI-trained models on government-provided databases. The government must comply by providing databases to train the AI, ensuring the models can accurately verify the legitimacy of identity cards. Once verified, the ID data is “burned” or securely discarded, ensuring that sensitive personal information is not stored or retained, further protecting user privacy.
Step 4: Government Compliance and Database Integration
for governments to utilize RiZKI, they must comply with providing access to databases needed to train AI models. This integration is crucial for the AI to accurately verify identity documents. Governments must ensure that the data provided is up-to-date and comprehensive. In return, this system benefits governments by reducing identity fraud and enhancing the security of identity verification processes.
Step 5: Continuous Monitoring and Adaptation
the verification process does not end with a single check. RiZKI continuously monitors user behavior and updates risk scores in real-time. If new anomalies or suspicious activities are detected, the system can prompt re-verification or additional security checks. This dynamic approach ensures ongoing security and minimizes the chances of fraudulent activity slipping through the cracks.
Benefits and Implementation
implementing RiZKI offers several benefits. Users experience a smoother, quicker verification process tailored to their risk profile, enhancing user satisfaction. Governments and organizations benefit from reduced fraud and increased trust in digital identity verification systems. By exploiting AI and ZKP capabilites, RiZKI may seudoidentify money laundering and vicious transactional behavior in order to get a both secure and privacy-preserving new standard in digital identity management.
Conclusions
the Risk-Based Zero Knowledge Identity (RiZKI) approach represents, indeed, a risky but evolutionary step forward of identification practices in DeFi and P2P market. RiZKI has a chance to alleviate secure user experience for all participants in the cyphered ecosystem, particularly advantageous for migrants above the globe.
