Tokenomics
Last updated
Last updated
In the burgeoning world of blockchain and data management, an innovative system has emerged, revolutionizing the way data is stored, shared, and valued. This system is predicated on the integration of blockchain technology with the principles of data science, creating an ecosystem where data owners, annotators, and curators interact dynamically, fostering an environment of collaboration and mutual benefit.
In this innovative system, data owners contribute raw or annotated data, which they can then mint into DBA, by spending $SOL. These DBAs collectively form a DBA Pool, which grows over time and rewards contributors with The Schema tokens. Curators play a critical role by evaluating available information—such as data samples or previous training outcomes—to compile a portfolio of cDBA for specific tasks. By investing $SOL, these portfolios are permanently recorded on the chain, and curators also receive token rewards for their efforts.
At the heart of this ecosystem is the concept, a transformative approach to treating data as a tangible asset. Data owners, ranging from individual researchers to communities and companies, contribute their raw or annotated data to the system. This data, once uploaded, undergoes a process of minting, where it is converted into DBAs by spending a certain amount of $SOL ensuring stability in transactions. DBAs represent a paradigm shift, offering a unique and immutable record of the data's existence, ownership, and attributes on the blockchain.
As more data Owners participate, the variety and volume of DBAs increase, contributing to the value growth of DBA Pool. This pool serves as a repository of valuable data assets, accessible to users across the ecosystem for various applications, from training machine learning models to conducting complex data analyses. The beauty of DBA Pool lies in its diversity and expansiveness, accommodating data across myriad domains and disciplines.
A key feature of this system is the issuance of token rewards to contributors. By contributing data to the pool, owners are not only recognized for their contribution but are also rewarded with tokens issued by the system. These tokens can be used within the ecosystem for transactions or exchanged in the broader crypto market, providing real-world value to contributors. This incentive mechanism ensures a continuous flow of high-quality data into DBA Pool, enriching the ecosystem and enhancing its utility for all users.
Curators, with their expertise in data analysis and domain knowledge, play a pivotal role in this ecosystem. They evaluate the available information, such as data samples, metadata, and previous training outcomes, to compile a portfolio of DBAs tailored for specific tasks or projects. This curatorial process involves a deep understanding of the data's potential applications and the needs of the end users, be they data scientists, researchers, or businesses seeking insights from the data.
Once a curated portfolio is compiled, it undergoes a process similar to that of individual DBA pieces, where it is minted into a cDBA through an investment of $SOL. This action not only permanently records the portfolio on the blockchain, providing a transparent and immutable record of its composition and purpose but also signifies the curator's role in adding value to the ecosystem. Like data owners, curators receive token rewards for their efforts, acknowledging their critical contribution to the system's functionality and success.
This innovative system is self-sustaining, driven by the continuous contributions of data owners and the curatorial efforts of data experts. As the $Data Pool grows, it attracts more users, further increasing the demand for curated data portfolios and incentivizing more contributions. This virtuous cycle fosters a vibrant, ever-expanding ecosystem where data is not just shared but valued as a critical asset.
One of the system's paramount benefits is the enhancement of data privacy and security. By leveraging blockchain technology, data transactions and ownership records are secured through encryption, ensuring that data remains confidential and tamper-proof. This level of security is especially crucial for sensitive or proprietary data, providing peace of mind for data Owners and users alike.
As this ecosystem continues to evolve, it is set to redefine the landscape of data management and utilization. The integration of blockchain technology with data science opens up new avenues for data monetization, privacy-preserving data analysis, and collaborative research. This system not only provides a framework for the ethical and efficient use of data but also highlights the untapped potential of blockchain in creating value from data in ways previously unimagined.
In essence, this innovative system marks a significant milestone in the journey towards a more open, secure, and equitable data economy. It stands as a testament to the power of collaboration and technology in unlocking the true value of data, paving the way for a future where data assets are universally accessible and valued.
Model trainers, utilizing the curated cDBA, conduct online training through federated learning on controlled cloud servers owned by the data providers. This process ensures privacy as gradients are homomorphically encrypted, aggregated, and then sent back to the trainers who decrypt and update their models. Compensation for data usage is facilitated through smart contracts that distribute tokens to the data owners.
In the dynamic landscape of machine learning, the process of model training has undergone a transformative evolution, propelled by innovations such as federated learning and blockchain technology. In this ecosystem, model trainers leverage cDBAs to conduct online training through federated learning methodologies. This approach, facilitated on controlled cloud servers owned by the data providers, represents a paradigm shift in the way models are trained, ensuring both privacy and efficiency.
The Essence of Federated Learning Federated learning embodies a decentralized approach to model training, where the training process occurs locally on distributed devices or servers. In the context of our ecosystem, model trainers utilize cDBAs as the foundation for this federated learning paradigm. Each data provider maintains control over their data, ensuring privacy and security while contributing to the collective learning process.
Privacy-Preserving Techniques Central to the federated learning process is the implementation of privacy-preserving techniques, chief among them being homomorphic encryption. Gradients generated during the training process are homomorphically encrypted, ensuring that sensitive data remains confidential throughout transmission and processing. This encryption scheme allows gradients to be aggregated securely without compromising the privacy of individual data points.
Aggregation and Decryption Once gradients are homomorphically encrypted and aggregated on controlled cloud servers owned by the data providers, they are sent back to the model trainers. Trainers then decrypt these aggregated gradients using cryptographic keys, allowing them to update their models with the collective insights gleaned from cDBA. This process seamlessly integrates privacy protection with model improvement, ensuring that advancements in machine learning are achieved without compromising data security.
Driving Innovation and Collaboration By combining federated learning with blockchain technology, this ecosystem drives innovation and collaboration in the field of machine learning. It empowers stakeholders to harness the collective intelligence of diverse datasets while preserving privacy and security—a critical balance in today's data-driven world. Moreover, compensating data providers fairly and transparently incentivizes the sharing of data and fosters a culture of collaboration and knowledge exchange.
Model trainers have the option to tokenize their AI models, which are then made available to users.
Computing Power can be tokenized and contributed by any entity, ranging from individual devices to national computing facilities.
This end-to-end ecosystem not only provides a transparent and equitable tool for data contribution, computing power contribution, and AI model development but also ensures privacy and rewards all participants within the blockchain framework.
