ZTips

Description

Problems: 1. Lack of Reliable Anonymous Tipping verification mechanisms leading to malicious reporting. 2. Lack of mature Technology Infrastructures increases the chance of information leakage and traceability of the tipster. 3. Risk to Life, Family, or Finances discourages participation. 4. Ineffective Incentive Mechanisms reduce Tipsters’ Motivation. Solutions: 1. Verification and Anonymity of Investigators and Tipsters is obtained through ZK implementation along with ZK Verification Badges with Optional KYC. 2. Incremental incentive release by Investigators leads to competition among tipsters to deliver credible information. 3. Freemium tip reporting & Credibility rating of tipsters lead to the enforcement of a feedback loop which eliminates malicious reporting. 4. Implementation of ZK for both anonymity and verification at multiple steps during the information exchange ensures a robust solution. 5. Offchain computation of entire proof generation ensures the scalability of the platform in handling high volumes of information exchange in the future. 6. Escrow implementation between investigator and tipster ensures protection from fraud risks. Dynamic Credibility System: 1. Acceptance Score: Tipsters are scored based on the ratio of accepted tips to total tips 2. Credibility Ratings: Tipsters are awarded based on their acceptance scores: A) Super Reliable: Tipsters with an accuracy score of over 80% are deemed highly credible. B) Trustworthy: Tipsters with scores between 60% and 80% are considered reliable. C) Considerable: Scores between 40% and 60% indicate moderate credibility. D) Risky: Tipsters with scores between 10% and 40% are flagged as potentially unreliable. E) Fraudster: Tipsters with scores below 10% are considered untrustworthy. Industry Use Cases: 1. Crime & Justice Delivery 2. Politics 3. Relationships 4. Software Bug Bounties/Ethical Hacking 5. Financial Institutions 6. Big Corporations 7. Surveys 8. Media Houses 9. Defense