Alberto Sonnino (University College London (UCL)), Mustafa Al-Bassam (University College London (UCL)), Shehar Bano (University College London (UCL)), Sarah Meiklejohn (University College London (UCL)), George Danezis (University College London (UCL))

Coconut is a novel selective disclosure credential scheme supporting distributed threshold issuance, public and private attributes, re-randomization, and multiple unlinkable selective attribute revelations. Coconut integrates with Blockchains to ensure confidentiality, authenticity and availability even when a subset of credential issuing authorities are malicious or offline. We implement and evaluate a generic Coconut smart contract library for Chainspace and Ethereum; and present three applications related to anonymous payments, electronic petitions, and distribution of proxies for censorship resistance.
Coconut uses short and computationally efficient credentials, and our evaluation shows that most Coconut cryptographic primitives take just a few milliseconds on average, with verification taking the longest time (10 milliseconds).

View More Papers

A Treasury System for Cryptocurrencies: Enabling Better Collaborative Intelligence

Bingsheng Zhang (Lancaster University), Roman Oliynykov (IOHK Ltd.), Hamed Balogun (Lancaster University)

Read More

Send Hardest Problems My Way: Probabilistic Path Prioritization for...

Lei Zhao (Wuhan University), Yue Duan (University of California, Riverside), Heng Yin (University of California, Riverside), Jifeng Xuan (Wuhan University)

Read More

CRCount: Pointer Invalidation with Reference Counting to Mitigate Use-after-free...

Jangseop Shin (Seoul National University and Inter-University Semiconductor Research Center), Donghyun Kwon (Seoul National University and Inter-University Semiconductor Research Center), Jiwon Seo (Seoul National University and Inter-University Semiconductor Research Center), Yeongpil Cho (Soongsil University), Yunheung Paek (Seoul National University and Inter-University Semiconductor Research Center)

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