Dangerous data manipulation fueling AI as a hazard
In a world where digital systems are managing transportation, industry, and urban processes, the integrity of these automated systems is paramount. One of the potential threats to these systems is data poisoning, a malicious practice that involves systematically feeding incorrect or specially prepared information into automated systems. Over time, algorithms begin to perceive these distortions as normal, leading to incorrect actions.
To combat this threat, the synergy between blockchain and federated learning is proving to be a powerful solution. Federated learning, a method where AI is trained on distributed sources without concentrating all data in one place, reduces the likelihood of a single attack impacting the entire system at once. This decentralized approach is further bolstered by blockchain technology.
Blockchain replaces the centralized server in federated learning with a decentralized ledger where clients collaboratively validate and reach consensus on a "judge model" that detects poisoned data or malicious model updates. This judge model is agreed upon through blockchain-enabled consensus, ensuring that no single server is fully trusted, thus mitigating centralized vulnerability exploited by poisoners.
Frameworks like FIDELIS implement this by having clients produce and verify a judge model that detects anomalies in other clients’ model updates, based on gradient movement patterns extracted from benign training data. The blockchain ledger maintains an immutable record of model updates and detection outcomes, facilitating transparency and accountability.
Research at Florida International University demonstrates that blockchain can flag outlier updates in federated learning and discard potential poisoned data before it reaches the global model, providing real-time defense in critical applications like autonomous driving and infrastructure management.
The combination of federated learning and blockchain creates a multi-layered defense against data poisoning attacks. While it's difficult to completely eliminate the risk of data poisoning, basic measures include limiting the volume of information processed, strictly checking incoming data against set criteria, and early detection of anomalies. Automatic update synchronization mechanisms in blockchain help detect suspicious patterns before they are integrated into the model.
The quality of decisions made by these technologies directly depends on the accuracy of the information they receive. Timely notification of operators and the ability to revert to verified model versions increase the AI's resilience to manipulation attempts. Blockchain can be employed to track the origin and history of data changes, enabling timely identification of the infection source and prevention of its spread.
It's worth noting that Australia has refused to weaken protection in favor of AI. The application of decentralized technologies and transparent control mechanisms enhances the systems' ability to resist malicious influences while maintaining their accuracy and security in information processing.
In summary, the synergy between blockchain and federated learning improves AI resilience against data poisoning by decentralizing trust, enabling collaborative, automated poison detection, ensuring immutability and transparency, improving privacy and scalability, and providing a robust defense against data poisoning attacks.
Artificial-intelligence systems, in a world mananged by digital systems, require the integrity of their automated counterparts, particularly to avoid data poisoning. The collaboration between blockchain and federated learning is proving to be an effective solution against data poisoning, as it enables a decentralized, collaborative, and automated poison detection mechanism.
