✈️Key Technique
1. xOfferID
The existing digital identity system faces significant challenges, impacting those who possess it negatively, and a substantial portion of the global population there are millions of peoples lacking any form of digital or legal identity. In the Web2 world, we are using a siloed shared-secret model such as username and password or federated identity of social networks which may not absolutely prioritize your data privacy. While centralized identity is attempting to address this issue, they inadvertently generate a new set of problems by consolidating vast amounts of information and control in a centralized manner.
xOfferID is truly built on a decentralized identity framework that is straightforward, secure, transparent, and ultimately governed by the individual it pertains to. The emerging domain of decentralized digital identity appears to offer a promising solution to these pressing issues. Join xOffer Platform, a digital world, you will obtain a xOfferID that allows you to become xOfferCitizen and start earning the rewards by completing tasks, quests or refer new users as helping the platform to bring more citizens. The immersive experience to help end-users connect from Web2 to Web3 is the purpose of this development phase until we completely move to Web3.
2. xOfferCitizen
xOfferCitizen plays an incredibly significant role within the organizational framework of xOffer Network, resulting from the harmonious fusion of the SoulBound Token electronic passport, the decentralized identity management system xOfferID, and the affiliate marketing system. This synergy positions xOfferCitizen as the repository for all user-related information.
The role of xOfferCitizen extends beyond mere information storage, encompassing the potential for utilizing these resources within the Decentralized Affiliate Network(DAN) system. This empowers partners to leverage the information for effectively implementing their marketing strategies, product distribution, and services.
By intelligently constructing and maintaining the xOfferCitizen system, xOffer Network is ceaselessly driving the advancement of decentralized applications and digital economic systems. Furthermore, it initiates an environment of interaction and collaboration among potential projects with suitable customer profiles.
3. Token Gating
Token gating is a verification method whereby communities can provide exclusive access to spaces, events, content, and communities to people who own specific digital assets in their wallet. It’s Offer different affiliate rewards based on what tokens users hold.
Why is token gating a good web3 marketing strategy?
Token gating is essential to the success of any web3 marketing strategy because it is one of the most effective ways to strengthen a community among token holders. Some benefits of token gating include:
1. Creates Exclusivity
Token gating generates exclusivity for token holders, and allows project creators to grant access to content, events, and merch exclusively to token holders.
Token gating benefits both creators and token holders:
Creators have greater control over who can access their community's products and content
Token holders benefit by getting preferential access based on their level of support
The exclusivity that token gating creates is exemplified by The Block’s tokenized paywall access to exclusive articles. The Block uses Access Protocol and issues Access Tokens on crypto marketplaces for consumers to purchase. Access Token holders are then able to access The Block’s exclusive subscriptions.
2. Strengthens Community
Token gating builds community among token holders. By providing access to token holders-only communication channels like Discord and content like Alchemy University's free Ethereum Developer bootcamp, token gating allows holders to congregate and develop a sense of community.
3. Rewards Holders
Token gating allows token holders to access special rewards. For instance, token holders can receive exclusive merch or acquire entrance into special festivals and events.
4. Anti-fraud Tracking System (AFTS)
AFTS system is used to not only stop malicious bots and prevent fraud threats before it impacts to our ecosystem but also detect fraudulent activity. In particular, to detect fraud, there are several techniques that we are implementing:
Calculate statistical parameters
Match data
Perform Logistic Regression analysis
Use probability models and distributions
Mine data to classify, segment, and cluster data to find associations and rules signaling patterns of malicious activities
Detect fraud with heuristic rules, machine learning and thresholds
AFTS system will need data to get started. The more data we feed into the system to start with, the more accurate results will be. Therefore, simulated fraud data is also feed into the system to ensure the volume, quality and diversity in analysis process.
The following information describes our major components in AFTS system:
Feeding the input data to AFTS system: The data we collect such as IP Address, Browsers, Device Type, Proxy (if use), transaction on chain, campaign identifier number, types of requests, etc. is used to build “digital avatars” or fingerprints. For the quest that needs a Sheriff to verify the action, the data is gathered to provide input to scoring function.
Rule generation: Before we have ML/AI, using heuristic rules is one of the most efficient way. The are two main types of rules that we use in AFTS system:
Single parameter rules, also known as heuristic rules.
Complex rules, including multiple parameters. We can adjust accuracy thresholds to tighten or loosen triggering conditions. The historical data will be used to help us to create a confusion matrix based on collected data over the selected time frame and highlights the estimated accuracy rate of those rules.
Fraud scoring and analytics: AFTS system leverages on machine learning models to assign risk scores to activities or user accounts based on various factors, such as transaction, location, frequency, and past behavior. Higher risk scores indicate a higher likelihood of fraud, enabling us to perform further investigation. Fraudulent actors often collaborate and form networks to carry out their activities. Another machine learning technique we implemented in AFTS system is semantic graph analysis to help uncover these networks by analyzing relationships between entities (such as users, wallets, transactions on chain, etc.) and identifying unusual connections or clusters.
Risk threshold settings: Finding the optimal risk threshold requires conducting data analysis rooted in the principles of “precision vs recall”. Setting the right limits for Allow/Review/Prevent thresholds depends on this model evaluation metrics. It entails a balancing act involving:
True positives (the number of fraudsters successfully blocked)
False positives (the number of legitimate individuals mistakenly blocked)
False negatives (the number of fraudsters inadvertently allowed)
Navigating this intricate balance is crucial for effective risk management in fraud detection and prevention. AFTS system leverage on LLM to understand the context and requirements from a natural language of administrator or business partners to suggest the right thresholds setting.
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