Blockchain is an immutable and distributed ledger managed by all participants, enhancing data transparency and safety. Immutability is a crucial factor in ensuring data transparency and safety. However, there is a significant demand for redaction of the ledger due to security and privacy concerns. In this paper, we propose a redactable blockchain solution based on meta-transactions using zk-SNARK to improve anonymity in a decentralised manner. A one-time cryptographic key generation scheme, designed for a signature generation scheme, produces different keys for each transaction to enhance security and privacy by preventing identity tracing. We also employ zk-SNARK to hide the information of cryptographic keys and signatures. The modification history for each transaction is linked together, and the verification time is significantly short, around 10 msec, even when transactions have multiple modifications. Furthermore, we introduce a redaction fee scheme for transaction owners to maintain concise modification histories encouraging removal instead of modification to minimise the performance overhead associated with this redaction approach.

Blockchain full nodes are pivotal for transaction availability, as they store the entire ledger, but verifying their storage integrity faces challenges from malicious remote storage attacks such as Sybil, outsourcing, and generation attacks. However, there is no suitable proof-of-storage solution for blockchain full nodes to ensure a healthy number of replicas of the ledger. Existing proof-of-storage solutions are designed for general-purpose settings where a data owner uses secret information to verify storage, rendering them unsuitable for blockchain where proof-of-storage must be fast, publicly verifiable, and data owner-agnostic. This paper introduces a decentralised and quantum-resistant solution named Non-interactive Practical Proof of Storage (nPPoS) with an asymmetric encoding and decoding scheme, for fast and secure PoStorage, and Zero-Knowledge Scalable Transparent Arguments of Knowledge (zk-STARKs), for public variability in blockchain full nodes. The algorithm with asymmetric times for encoding and decoding creates unique block replicas and corresponding proofs for each storage node to mitigate malicious remote attacks and minimise performance degradation. The intentional resource-intensive encoding deters attacks, while faster decoding minimises performance overhead. Through zk-STARKs, nPPoS achieves public verifiability enabling one-to-many verification for scalability, quantum resistance and decentralisation. It also introduces a two-phase randomisation technique and a time-weighted trustworthiness measurement for scalability and adaptability.

Blockchain is a distributed and immutable ledger managed by all participants. The full nodes which store the entire ledger play an essential role in managing it in a transparent and decentralised manner. However, it is difficult to verify that full nodes store the entire ledger in their dedicated storage due to Sybil, outsourcing, or generation attacks. Existing work on proving storage for cloud computing and remote data storage applications has high latency for decryption, and its impact on decentralisation is unclear, rendering it impractical for use in blockchain. In this paper, we propose a decentralised Practical Proof of Storage (PPoS) solution for blockchain full nodes with asymmetric latencies for encryption and decryption, which introduces a chained encryption and decryption architecture. To generate a unique replica of a block, each full node performs encryption with its own address and a previously encrypted block, storing the unique block in its dedicated storage. In PPoS, encryption is expensive and time consuming, enabling it to detect outsourcing and generation attacks and to deter Sybil attacks. Simultaneously, decryption is about 25 times faster than encryption, resulting in minimal performance overhead. The proof process is also decentralised by randomly selecting provers, verifiers, and encrypted blocks. Our experiments use up to 720 real BitCoin blocks to evaluate the performance and quantify the decentralisation of PPoS. Our results show that PPoS's asymmetric design reduces decryption time 25-fold over existing approaches, while maintaining a high degree of decentralisation, confirming its suitability for blockchain full nodes.

Blockchain has attracted considerable attention as a solution to the challenges of privacy, security and decentralisation for many applications. However, these characteristics of the blockchain result in ever growing ledger size, which is one of the major barriers to blockchain adoption in large-scale networks such as the Internet of Things (IoT). In this paper, we propose Multi-Level Distributed Caching (MLDC) for blockchain storage optimisation which reduces data replication based on data access pattern. MLDC divides nodes into storage classes (SCs) by their node availability, and assigns each SC a different Access Frequency (AF) to remove data from the local storage. Over time, each node only stores frequently accessed data, so MLDC can reduce the total storage cost by 83% compared to conventional blockchain systems, while maintaining blockchain consistency and data availability with a slight increase in network overhead and data query delay.

Distribution, security, and immutability have led to the great success of blockchain in many applications, while contributing to major increases in ledger size. The storage challenge is one of the major barriers to the adoption of blockchain in the Internet of Things (IoT), which consists of many resource constrained devices. In this paper, we propose Multi-Level Distributed Caching (MLDC) for blockchain storage optimisation which reduces data replication based on data access patterns in a decentralised manner. For storage optimisation of data-centric blockchains, MLDC introduces a hierarchical storage class (SC), in which every node is assigned to an SC with its own Access Frequency (AF) threshold based on node availability. To reduce the number of replications shared among participant nodes, each node in a SC continues to remove unaccessed data from local storage based on a threshold time determined by the AF threshold of the SC, while maintaining all block hashes for consistency. Eventually, all nodes in MLDC store the most frequently accessed data in their local storage, so MLDC effectively reduces the storage and query costs while minimising network overhead. We also analyse the security of MLDC and quantitatively evaluate its performance for both the uniform access and exponentially decaying access patterns. The evaluation was carried out on a representative blockchain simulator with 15 storage nodes. Our results from 11 hours of experiments producing 6667 blocks and 39997 transactions show good performance for MLDC. The results of the experimentation for the exponentially decaying assess pattern show that MLDC can reduce the total storage cost by 83% compared to conventional blockchain systems, while maintaining blockchain consistency and data availability with a slight increase in network overhead and query cost.