Blockchains guarantee data integrity through consensus of distributed ledgers based on multiple validation nodes called miners. For this reason, any blockchain system can be critically disabled by a malicious attack from a majority of the nodes (e.g., 51% attack). These attacks are more likely to succeed as the number of nodes required for consensus is smaller. Recently, as blockchains are becoming too large (making them difficult to store, send, receive, and manage), sharding is being considered as a technology to help improve the transaction throughput and scalability of blockchains. Sharding distributes block validators to disjoint sets to process transactions in parallel. Therefore, the number of validators of each shard group is smaller, which makes shard-based blockchains more vulnerable to 51% attacks than blockchains that do not use sharding. To solve this problem, this paper proposes a trust-based shard distribution (TBSD) scheme that assigns potential malicious nodes in the network to different shards, preventing malicious nodes from gaining a dominating influence on the consensus of a single shard. TBSD uses a trust-based shard distribution scheme to prevent malicious miners from gathering in on one shard by integration of a trust management system and genetic algorithm (GA). First, the trust of all nodes is computed based on the previous consensus result. Then, a GA is used to compute the shard distribution set to prevent collusion of malicious miners. The performance evaluation shows that the proposed TBSD scheme results in a shard distribution with a higher level of fairness than existing schemes, which provides an improved level of protection against malicious attacks.
|Number of pages||12|
|Publication status||Published - 2019|
Bibliographical noteFunding Information:
This work was supported by the Ministry of Science and ICT (MSIT), South Korea, through the Information Technology Research Center (ITRC) Support Program supervised by the Institute for Information and Communications Technology Planning and Evaluation (IITP) under Grant IITP-2019-2018-0-01799.
© 2013 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Materials Science(all)