Privacy-preserving Payment Channel Networks using Trusted Execution Environment

Peng Li, Song Guo, Toshiaki Miyazaki

Research output: Unpublished conference presentation (presented paper, abstract, poster)Conference presentation (not published in journal/proceeding/book)Academic researchpeer-review

Abstract

Payment channel networks (PCN) have demonstrated its significant advantages in improving the scalability of blockchain. However, the existing work of PCN leads to serious privacy leakage problem that intermediate nodes along a payment path can collude to obtain the payment amounts and payment receivers. To address this problem, we propose to move PCN-related modules into the Trusted Execution Environment (TEE) commonly available on modern CPUs, so that adversaries cannot access the critical payment information protected by TEE, even though they compromise the software (e.g., blockchain clients or operating system) outside of TEE. An additional challenge is that adversaries can still infer payment receivers by observing the pattern of message transmissions among nodes. To hide payment receivers, we further propose to send redundant transactions to pseudo receivers to confuse adversaries. A fast algorithm with provable approximation ratio has been proposed to maximize the level of privacy protection under the constraint of communication overhead. Both experiments on a small-scale testbed and large-scale simulations are conducted to evaluate our proposal. The results show that our proposed solution outperforms existing work significantly.
Original languageEnglish
Pages1-6
Number of pages6
Publication statusPublished - Jun 2020
Event Communication & Information Systems Security Symposium (CISS) of IEEE ICC 2020 -
Duration: 7 Jun 202011 Jun 2020
https://icc2020.ieee-icc.org/

Conference

Conference Communication & Information Systems Security Symposium (CISS) of IEEE ICC 2020
Period7/06/2011/06/20
Internet address

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