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

coucouArray ( [post_type] => ndss-paper [post_status] => publish [posts_per_page] => 4 [orderby] => rand [tax_query] => Array ( [0] => Array ( [taxonomy] => category [field] => id [terms] => Array ( [0] => 34 ) ) ) [post__not_in] => Array ( [0] => 4699 ) )

NAUTILUS: Fishing for Deep Bugs with Grammars

Cornelius Aschermann (Ruhr-Universität Bochum), Tommaso Frassetto (Technische Universität Darmstadt), Thorsten Holz (Ruhr-Universität Bochum), Patrick Jauernig (Technische Universität Darmstadt), Ahmad-Reza Sadeghi (Technische Universität Darmstadt), Daniel Teuchert (Ruhr-Universität Bochum)

Read More

Constructing an Adversary Solver for Equihash

Xiaofei Bai (School of Computer Science, Fudan University), Jian Gao (School of Computer Science, Fudan University), Chenglong Hu (School of Computer Science, Fudan University), Liang Zhang (School of Computer Science, Fudan 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

NIC: Detecting Adversarial Samples with Neural Network Invariant Checking

Shiqing Ma (Purdue University), Yingqi Liu (Purdue University), Guanhong Tao (Purdue University), Wen-Chuan Lee (Purdue University), Xiangyu Zhang (Purdue University)

Read More

Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)