How does it works

AI Companies Buy User Data

The BCA Protocol creates a user-generated database of valuable data. This data is sold to official partners like OpenAI and Stable Diffusion, who use it to train their AI models.

You Earn by Contributing with Your Data to the Network

As a user, you can contribute data by connecting your devices to the BCA Protocol network. You have full control over the data you choose to share. By contributing your data, you earn a portion of the revenue generated from selling this data to AI companies.

Completely private and the data never leaves your device

Your privacy and data ownership are of utmost importance. The BCA Protocol uses Zero-Knowledge Proof (ZK proof) technology to ensure that your identity remains completely anonymous. Federated learning is implemented, meaning that only the model updates are shared, not the actual data itself. This guarantees that your personal information stays private and secure.

Process Flow

The BCA Protocol integrates data collection, federated learning, blockchain technology, and robust privacy measures to create a secure and rewarding ecosystem for AI development.

1. Data Collection: Agents on user devices gather and pre-process data, ensuring anonymity.

2. Federated Learning: Local Model Trainers update AI models using local data, with updates sent to the Model Aggregator.

3. Privacy and Security: Data is anonymized, verified using ZKPs, and encrypted to maintain privacy and security.

4. Blockchain Integration: Contributions and updates are recorded on the blockchain for transparency and reward management.

5. Reward Distribution: Calculated based on contributions and distributed via smart contracts to users' wallets.

This flow ensures that user data is collected and utilized in a secure, private, and transparent manner, while rewarding users for their valuable contributions.

The type of data users can contribute with

Individual users can contribute various types of data through the BCA Protocol. The data can be broadly categorized into several key areas:

1. Browsing Data:

• Web Activity: Users' interactions with websites, including page visits, time spent on pages, and click patterns.

• Search Queries: Anonymized data on search queries which helps in understanding trends and user interests.

2. Sensor Data:

• IoT Devices: Data from smart home devices such as thermostats, smart speakers, and security systems.

• Mobile Sensors: Data from smartphone sensors including accelerometers, GPS, and gyroscopes, which can provide insights into movement patterns and location-based trends.

3. Application Usage

• App Interaction: Information on how users interact with mobile and desktop applications, including usage frequency and feature utilization.

• Performance Metrics: Data on app performance, including load times, error rates, and user satisfaction metrics.

4. Health and Fitness Data:

• Wearables: Data from fitness trackers and smartwatches, such as heart rate, steps taken, and sleep patterns.

• Health Apps: Information from health-related applications that track diet, exercise, and general wellness.

5. E-commerce Data:

• Shopping Behavior: Insights into online shopping habits, including product searches, purchase history, and cart abandonment rates.