Derek Leung (MIT CSAIL), Adam Suhl (MIT CSAIL), Yossi Gilad (MIT CSAIL), Nickolai Zeldovich (MIT CSAIL)

Decentralized cryptocurrencies rely on participants to keep track of the state of the system in order to verify new transactions. As the number of users and transactions grows, this requirement becomes a significant burden, requiring users to download, verify, and store a large amount of data to participate.

Vault is a new cryptocurrency design based on Algorand that minimizes these storage and bootstrapping costs for participants. Vault’s design is based on Algorand’s proof-of-stake consensus protocol and uses several techniques to achieve its goals. First, Vault decouples the storage of recent transactions from the storage of account balances, which enables Vault to delete old account state. Second, Vault allows sharding state across participants in a way that preserves strong security guarantees. Finally, Vault introduces the notion of stamping certificates, which allow a new client to catch up securely and efficiently in a proof-of-stake system without having to verify every single block.

Experiments with a prototype implementation of Vault’s data structures show that Vault’s design reduces the bandwidth cost of joining the network as a full client by 99.7% compared to Bitcoin and 90.5% compared to Ethereum when downloading a ledger containing 500 million transactions.

View More Papers

Distinguishing Attacks from Legitimate Authentication Traffic at Scale

Cormac Herley (Microsoft), Stuart Schechter (Unaffiliated)

Read More

Tranco: A Research-Oriented Top Sites Ranking Hardened Against Manipulation

Victor Le Pochat (imec-DistriNet, KU Leuven), Tom Van Goethem (imec-DistriNet, KU Leuven), Samaneh Tajalizadehkhoob (Delft University of Technology), Maciej Korczyński (Grenoble Alps University), Wouter Joosen (imec-DistriNet, KU Leuven)

Read More

Geo-locating Drivers: A Study of Sensitive Data Leakage in...

Qingchuan Zhao (The Ohio State University), Chaoshun Zuo (The Ohio State University), Giancarlo Pellegrino (CISPA, Saarland University; Stanford University), Zhiqiang Lin (The Ohio State University)

Read More

Neural Machine Translation Inspired Binary Code Similarity Comparison beyond...

Fei Zuo (University of South Carolina), Xiaopeng Li (University of South Carolina), Patrick Young (Temple University), Lannan Luo (University of South Carolina), Qiang Zeng (University of South Carolina), Zhexin Zhang (University of South Carolina)

Read More