We live in a time when data has become a valuable resource that is incredibly vulnerable. Evidence of this is what happened in Italy recently, where even facilities that theoretically should protect citizens from cyber-attacks have become victims of unprecedented data theft. We entrust personal and sensitive items daily to large companies and institutions, trusting that they will be handled responsibly and securely. However, reality shows us that this centralised data management model has significant limitations. Privacy breaches, cyber-attacks and misuse of access to records have become all too common, putting our security and freedom at risk.
Fortunately, technological innovation offers us new solutions to address these challenges. Decentralisation is emerging as an alternative paradigm for data management, providing greater security, transparency and control for users. Bitcoin uses this same technology to generate value for all its users. Technologies such as blockchain and federated learning drive this revolution, offering powerful tools to protect our information and redefine the relationship between individuals and the digital. Blockchain, known primarily for its role in cryptocurrencies, is a versatile technology with enormous potential in several areas. The distributed and immutable digital ledger, where every transaction or information is recorded securely and transparently, enjoys a decentralised structure that eliminates the need for a central controlling authority, making the technology resistant to censorship and tampering.
Changing the model from the centre to the federation
Blockchain offers known guarantees of privacy and security. Sensitive data can be encrypted and distributed over a computer network, making it virtually inaccessible to criminals. In addition, the system’s transparency allows the origin and history of each piece of information to be tracked, ensuring data integrity and accountability.
Federated learning is another promising technology that fits perfectly with the decentralisation philosophy. It allows artificial intelligence models to be trained without centralising data. Instead of collecting all the information in one place, federated learning distributes the training process across multiple devices, such as smartphones or computers. Each device trains a local model on its own data and only shares model updates with a central server. This approach preserves data privacy, as sensitive information never leaves the user’s device. In addition, federated learning allows the computational power of millions of devices to be harnessed, accelerating the development of more accurate and high-performance artificial intelligence models. Each device trains a local model on its own data and only shares model updates with a central server. In this way, raw data is never transferred, dramatically reducing the risk of privacy breaches.
The issue of transparency
Another crucial aspect of decentralisation is transparency. Transparency is a critical element in building trust and promoting active user participation. Decentralisation improves security and privacy and can make systems more efficient by eliminating the need for intermediaries and reducing transaction costs. In addition, decentralised systems are more resilient because they do not depend on a single point of failure. If one node in the network is compromised, the others can continue to function normally, ensuring continuity of service.
