Trusted data flows in decentralised systems

Blockchain have been mentioned in different settings for years, but many don’t appreciate how important blockchain will become for cyber security. Therefore lets delve deeper into how blockchain contributes to enhancing data security and its potential applications: Data Integrity and Authenticity Every transaction on a blockchain is time-stamped and assigned a unique hash, ensuring that the data remains unchanged and authentic over time. This is particularly valuable in verifying the integrity of records without relying on a centralized authority. Permissioned vs. Permissionless Blockchains There are different types of blockchains tailored for varying needs. Permissionless (public) blockchains, like Bitcoin and Ethereum, allow anyone to join and validate the network, promoting transparency. Permissioned (private) blockchains restrict access to a limited number of users, providing greater control over who can view and alter the blockchain, often used by enterprises for enhanced privacy. Smart Contracts These are self-executing contracts with the terms of the agreement directly written into code. They automatically enforce and execute actions when predefined conditions are met, reducing the need for intermediaries and mitigating risks of manual processing errors. Security against Cyber Attacks Traditional centralized databases can be vulnerable to hacking attempts. However, due to its decentralized nature, attacking a blockchain requires overwhelming a majority of the network nodes simultaneously, which is resource-intensive and highly improbable in large public blockchains. Privacy through Cryptographic Algorithms Advanced cryptographic techniques are employed to protect user anonymity and sensitive information, even if all transactions are visible on the ledger. Methods like zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) enable proof of transaction validity without revealing underlying data. Interoperability with Existing Systems Blockchain can integrate with existing systems to enhance their security features. This can be seen in consortium blockchains, where multiple organizations within a specific industry collaborate and maintain a shared ledger to improve transparency and coordinate secure operations. Use Cases in Various Industries; Finance Securing financial transactions, reducing fraud, and enhancing transparency in auditing. Healthcare Securing patient records, ensuring privacy while maintaining accessibility amongst healthcare providers. Supply Chain Enhancing traceability of goods, ensuring authenticity, and reducing fraud within the supply chain. Voting Systems Providing transparent and tamper-proof election systems to ensure fair and free elections. Blockchain technology is constantly evolving, offering innovative solutions to data security challenges across various sectors while addressing key concerns of scalability, speed, and regulatory compliance. #blockchain #cybersecurity

#blockchain | #digitalidentity | #crossborder | #trade : "Unlocking Trade Data Flows with Digital Trust Using Interoperable Identity Technology" The paper reviews the current challenges in unlocking cross-border data flows, and how interoperability of digital identity regimes using high level types of decentralized technologies can overcome this with active public-private partnerships. Decentralized identity technologies, such as verifiable credentials (VCs) and decentralized identifiers (DIDs), coupled with interoperability protocols can complement the current Web3 infrastructure to enhance interoperability and digital trust . It is noted in the World Economic Forum White Paper that global trust worthiness is an important identity system principle for future supply chains, as this process of dynamically verifying counterparts through digital identity management and verification is a critical step in establishing trust and assurance for organizations participating in digital supply-chain transactions. As the number of digital services, transactions and entities grow, it is crucial to ensure that digitally traded goods and services take place in a secure and trusted network in which each entity can be dynamically verified and authenticated. Web3 describes the next generation of the internet that leverages blockchain to “decentralize” storage, compute and governance of systems and networks, typically using open source software and without a trusted intermediary. With the new iteration of Web3 being the next evolution of digitalized paradigms, several new decentralized identity technologies have become an increasingly important component to complement existing Web3 infrastructure for digital trade. VCs are an open standard for digital credentials, which can be used to represent individuals, organizations, products or documents that are cryptographically verifiable and tamper-evident. The important elements of the design framework of digital identities involves three parties – issuer, holder and verifier. This is commonly referred to the self sovereign identity (SSI) trust triangle. The flow starts with the issuance of decentralized credentials in a standard format. The holder presents these credentials to a service provider in a secure way. The verifier then assesses the authenticity and validity of these credentials. Finally, when the credential is no longer required, the user revokes it. This gives rise to the main applications of digital identities and VCs in business credentials, product credentials and document identifiers in the trade environment involving businesses, goods and services. EmpowerEdge Ventures

📣 A new paper from our lab (EdgeLab at UCI) to ensure that untrusted databases process SQL queries correctly on a private database using non-interactive zero-knowledge proofs (ZKPs) You can find the paper here (to appear in SIGMOD 2025): https://lnkd.in/gsaHrNYY The paper, led by soon-to-graduate PhD student Binbin Gu, introduces PoneglyphDB, a database system that can prove that SQL processing is both trusted and correct. This would enable use cases ranging from utilizing untrusted service providers to decentralized and offchain database processing infrastructures. The paper highlights a novel design of ZKP circuits optimized for fundamental SQL operations—such as joins, sorting, and group-by—by leveraging recent advances in cryptographic techniques like PLONKish circuits and recursive proof composition. PoneglyphDB demonstrates significant performance improvements over state-of-the-art ZKP-based approaches when benchmarked with the TPC-H workload. #Databases #ZKP #SIGMOD2025 #blockchain

Safeguard Data Privacy Concerns by combining Transfer and Federated Learning Techniques Transfer learning and federated learning are two powerful techniques revolutionizing the landscape of machine learning. While transfer learning leverages pre-trained models to accelerate training on new tasks, 𝐟𝐞𝐝𝐞𝐫𝐚𝐭𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 facilitates collaborative training across decentralized data sources without sharing raw data. Combining these methods presents a compelling approach to overcome the challenges of data heterogeneity and privacy concerns. 𝐓𝐫𝐚𝐧𝐬𝐟𝐞𝐫 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 involves transferring knowledge from a pre-trained model to a new task, significantly reducing training time and data requirements. Federated learning, on the other hand, enables model training across distributed data sources while preserving data privacy. Integrating transfer learning into federated learning enhances model performance and convergence speed in decentralized environments. 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬: 1. Transfer learning jumpstarts model training by leveraging pre-existing knowledge. 2. Federated learning aggregates diverse data sources, improving model generalization. 3. Federated learning ensures data privacy by keeping data decentralized and only sharing model updates. 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: 1. Combining data from different sources in federated learning requires addressing data distribution mismatches. 2. Federated learning involves communication overhead due to frequent model updates across devices. 3. Transfer learning may lead to model drift if the target task significantly differs from the pre-trained task. Integrating transfer learning into federated learning involves initializing models with pre-trained weights and fine-tuning them on decentralized data sources. This approach minimizes the need for extensive data sharing while leveraging the benefits of pre-trained representations. 𝐏𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝐔𝐬𝐞-𝐜𝐚𝐬𝐞𝐬: 1. Healthcare: Federated learning enables collaborative training of medical image classification models across hospitals while transfer learning enhances model performance on specific diseases. 2. Finance: Federated learning facilitates training fraud detection models across banking institutions, while transfer learning improves model accuracy for detecting new fraud patterns. 3. IoT: Federated learning enables collaborative training of predictive maintenance models across IoT devices, while transfer learning enhances model robustness to device-specific variations. In summary; by combining transfer learning and federated learning, we can harness the collective intelligence of decentralized data sources while leveraging pre-existing knowledge to improve model performance and preserve 𝐝𝐚𝐭𝐚 𝐩𝐫𝐢𝐯𝐚𝐜𝐲 in various real-world applications. #federatedlearning #transferlearning #datasecurity #dataprivacy #twominutedigest