MIT researchers have developed a new cryptocurrency that drastically reduces the data users need to join the network and verify transactions — by up to 99 percent compared to today’s popular cryptocurrencies. This means a much more scalable network. Cryptocurrencies, such as the popular Bitcoin, are networks built on the blockchain, a financial ledger formatted in a sequence of individual blocks, each containing transaction data. These networks are decentralized, meaning there are no banks or organizations to manage funds and balances, so users join forces to store and verify the transactions. But decentralization leads to a scalability problem. To join a cryptocurrency, new users must download and store all transaction data from hundreds of thousands of individual blocks. They must also store these data to use the service and help verify transactions. This makes the process slow or computationally impractical for some. In a paper being presented at the Network and Distributed System Security Symposium next month, the MIT researchers introduce Vault, a cryptocurrency that lets users join the network by downloading only a fraction of the total transaction data. It also incorporates techniques that delete empty accounts that take up space, and enables verifications using only the most recent transaction data that are divided and shared across the network, minimizing an individual user’s data storage and processing requirements. In experiments, Vault reduced the bandwidth for joining its network by 99 percent compared to Bitcoin and 90 percent compared to Ethereum, which is considered one of today’s most efficient cryptocurrencies. Importantly, Vault still ensures that all nodes validate all transactions, providing tight security equal to its existing counterparts. “Currently there are a lot of cryptocurrencies, but they’re hitting bottlenecks related to joining the system as a new user and to storage. The broad goal here is to enable cryptocurrencies to scale well for more and more users,” says co-author Derek Leung, a graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Joining Leung on the paper are CSAIL researchers Yossi Gilad and Nickolai Zeldovich, who is also a professor in the Department of Electrical Engineering and Computer Science (EECS); and recent alumnus Adam Suhl ’18.