By: Pinaki Sahu, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan, email@example.com
Blockchain is becoming popular for securing data records and with the alliance of artificial intelligence we can make it more advanced in revolutionizing secure data sharing. The combination of tamper-proof blockchain properties and advanced AI data analytics capabilities, organisations can establish a strong framework for protecting sensitive data. This article explores by traversing the key concepts, real-world applications and the promising future of this synergy.
Keywords: Blockchain, Artificial Intelligence, Secure Data Sharing, Data Privacy, Smart contracts
In this data driven world, the data fuels the engine of organization. Securing sharing information has evolved , not only a foundation but also an essential requirement for promoting collaboration, accelerating innovation, and maintaining competitive advantages. But nowadays traditional data sharing methods are falling short for ensuring confidentiality, integrity and authenticity. As we go beyond in this exploration to break the limitations, by embracing the convergence of two cutting-edge technologies: Blockchain and Artificial Intelligence(AI).These two pillars of innovation comes together with a sole purpose of changing the fundamental nature of safe data sharing, as well as tackling today’s difficulties.
Blockchain and Artificial Intelligence (AI) Synergy
Blockchain is a peer-to-peer distributed ledger that can only be updated by consensus or agreement among peers and is append-only, and immutable (very difficult to change). Blockchain is immune to single points of failure because of its decentralised nature, which does away with the necessity for a central authority. All network users can independently check transactions due to the transparency it provides, maintaining a level playing field. This decentralized ledger system serves as a foundation for improving trust and security in digital transactions.
On the other hand artificial intelligence has the capacity to analyse huge datasets and produce insightful results. Machine learning algorithms thrives on patterns, can spot anomalies, and can forecast the future using data-driven insights. AI extends into realms such as natural language processing, computer vision, and deep learning, where its versatility and scalability enable it to handle a wide range of jobs.
In this collaboration, Blockchain ssures that data is immutable, creating an atmosphere where trust is the mandatory. After being added to the blockchain, each piece of data is encrypted and made immutable by cryptographic seals. As the sentinel, AI patrols the blockchain network’s massive data store, carefully examining trends, spotting anomalies, and picking up potential threats. AI can identify abnormalities using machine learning models before they form as breaches, alerting stakeholders to take preventative measures.
Data Encryption and Authentication: Blockchain can be used to encrypt and authenticate data, guaranteeing that only people with the proper permissions can access it. AI algorithms that examine biometric information or behavioural patterns can improve authentication.
Smart Contracts: Automated and secure data sharing agreements are made possible by smart contracts on blockchain platforms. These contracts can be monitored and carried out by AI algorithms depending on defined parameters.
This flow chart represents the synergy of block chain and cybersecurity, explaining the key steps in the process:
- Blockchain Technology Checking if blockchain technology is used, highlighting its characteristics.
- AI Technology: Checking if AI technology is used, highlighting its capabilities.
- Combine Blockchain & AI: Expressing the intention to merge blockchain and AI.
- Synergy Assessment: Evaluating whether the combination results in a potent synergy.
Taxonomy of Block chain and AI
This taxonomy diagram visually represents the hierarchical relationship between the main categories (Technology, Blockchain, and AI) and their subcategories, providing an overview of the topics covered in these fields.
Applications of using both Blockchain and AI
Secure and Interoperable Health Records: Blockchain provides a secure and immutable ledger for storing patient health records. AI-powered healthcare applications may securely access these records, allowing healthcare providers to access patient data fast and precisely.
Privacy of Patient Data: The decentralised nature of blockchain gives patients more control over their data. AI algorithms can analyse this data with the patient’s consent, facilitating medical research, personalized treatment plans, and early disease detection.
Transparent and Efficient Transactions: Blockchain enables transparent and tamper-proof financial transactions. The ledger has an unchangeable history of financial operations and records every transaction. These transactions can be tracked in real-time by AI-powered analytics, which can also spot patterns and abnormalities that might point to fraudulent or suspicious activity.
Smart Contracts for Automation: Smart contracts on blockchain platforms automate financial agreements, such as loans, insurance claims, and trade settlements. By analysing external data sources and initiating contract activities depending on predefined circumstances, AI algorithms can improve the execution of smart contracts, increasing the effectiveness of financial processes.
Evaluation of risks and fraud detection: AI employs machine learning models to assess credit risk, detect fraudulent transactions, and identify unusual financial behaviour. As blockchain data offers a transparent and comprehensive perspective of an entity’s financial history, it can be included in AI algorithms to improve the accuracy of risk evaluation.
3.1 Quality Assurance: During the shipping of perishable items, the blockchain can store quality assurance information including temperature, humidity, and handling circumstances. AI algorithms can analyse this data to predict and prevent quality issues, reducing waste and ensuring product quality.
3.2 Supplier Verification: Blockchain can store and verify supplier credentials, certifications, and legal records. To maintain continuing compliance and reduce the risks associated with non-compliant vendors, AI can continuously monitor and evaluate supplier data.
3.3 Traceability and Transparency: Blockchain makes end-to-end traceability of products in the supply chain, from manufacturing to delivery. In order to optimise logistics and reduce delays, AI-powered analytics may use this data to provide real-time insights into the movement of goods.
Challenges and considerations
1.Scalability: A significant amount of computational power is needed for both blockchain and AI. Scalability problems may result from their integration, which might cause stress on the infrastructure. Blockchain networks, in particular, frequently struggle to manage a large volume of transactions or data, which can affect the way AI applications perform.
2.Data Privacy and Security: While AI mainly relies on data, which might raise privacy issues, blockchain is known for its security qualities. In certain situations, it might not be a good idea to store sensitive data on a blockchain because doing so might reveal private information to more people. A fundamental problem is finding a balance between the requirement for transparency and data privacy.
3.Data Quality: For accurate analysis and predictions, AI models need high-quality data. Although blockchain data is mostly accurate, mistakes or inconsistencies can still occur. Blockchain data needs to be of high quality when given to AI algorithms to avoid false conclusions.
4.Energy Consumption: Some blockchain networks, like Bitcoin, use a lot of electricity. Integrating AI systems with blockchain networks, which consume a lot of energy, may not be consistent with sustainability objectives and may have negative environmental effects.
5.Complexity: Adding blockchain and AI to IT ecosystems adds another level of complexity. It might be difficult to handle this complexity, including troubleshooting and guaranteeing system reliability.
6.Interoperability: Blockchain platforms are diverse, and there are many different types and frameworks for AI systems. Ensuring seamless interoperability between different blockchain networks and AI tools can be complex.
7.Ethical Considerations :As AI and blockchain become more connected, ethical issues relating to decision-making, accountability, and transparency become increasingly important. It is vital to ensure ethical and responsible AI use in a blockchain environment.
The alliance between blockchain and artificial intelligence (AI) will revolutionise secure data sharing. A strong framework for protecting sensitive data is provided by the immutability and transparency of blockchain technology as well as the data analytics capabilities of artificial intelligence. Though ,this synergy is not without difficulties.
Organizations must consider scalability, data privacy, and security issues as they manage the integration of various technologies. It is crucial to maintain data quality and deal with concerns related to energy use. The difficulties are made even more difficult by the complexity of managing these technologies and guaranteeing interoperability. Despite these obstacles, there are many and intriguing applications. In the healthcare industry, patient records may be shared safely, and AI supports diagnosis. Transparent transactions and fraud detection are beneficial to finance. Supply chains get quality assurance and traceability.
To fully utilise the potential of blockchain and AI in this dynamic environment and transform data sharing and security paradigms across businesses, it is important to overcome these difficulties. The necessity of responsible adoption is further highlighted by ethical considerations. To prepare for a data-centric future, we must strike a delicate balance between innovation and security as we move forward.
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Sahu P. (2023) Alliance between Blockchain and AI: An Approach to Secure Data Exchange, Insights2Techinfo, pp.1