DataS: Software for Data Collaboration and Monetization

The current policy landscape is pushing for the unlocking of data value and the development of new productive forces by promoting the market circulation of data elements to drive innovation. However, there is a significant gap in the market as high-quality data, which is crucial for AI development, is in short supply. This shortage stems from most companies lacking the necessary resources and expertise for effective data development and utilization. Additionally, the process of extracting valuable insights from raw data is complex, time-consuming, and labor-intensive, involving steps such as data management, cleaning, extraction, and integration. Without a standardized approach, these challenges lead to high costs and inefficiencies, preventing businesses from fully realizing the potential of their data assets.

The need for multi-party collaboration to combine datasets and enhance their value is increasingly apparent, but significant barriers remain. Current data systems and AI models often operate in silos, making it difficult to perform comprehensive analyses. Moreover, data protection is a major concern, as sharing sensitive information can lead to potential leaks. A federated learning collaborative framework addresses these issues by allowing data to remain within its local environment while enabling secure and efficient collaboration. By providing technology for seamless data management and AI-driven analysis, along with advanced tools like zero-knowledge proof and blockchain for data validation and tracking, the framework enables companies to unlock data value through secure, multi-party cooperation, paving the way for a new era of data-driven innovation.