Credit Network Payment Systems: Security, Privacy and Decentralization
Abstract
A credit network models transitive trust between users and enables transactions between arbitrary pairs of users. With their flexible design and robustness against intrusions, credit networks form the basis of Sybil-tolerant social networks, spam-resistant communication protocols, and payment settlement systems. For instance, the Ripple credit network is used today by various banks worldwide as their backbone for cross-currency transactions. Open credit networks, however, expose users’ credit links as well as the transaction volumes to the public. This raises a significant privacy concern, which has largely been ignored by the research on credit networks so far. In this state of affairs, this dissertation makes the following contributions. First, we perform a thorough study of the Ripple network that analyzes and characterizes its security and privacy issues. Second, we define a formal model for the security and privacy notions of interest in a credit network. This model lays the foundations for secure and privacy-preserving credit networks. Third, we build PathShuffle, the first protocol for atomic and anonymous transactions in credit networks that is fully compatible with the currently deployed Ripple and Stellar credit networks. Finally, we build SilentWhispers, the first provably secure and privacy-preserving transaction protocol for decentralized credit networks. SilentWhispers can be used to simulate Ripple transactions while preserving the expected security and privacy guarantees.
Degree
Ph.D.
Advisors
Kate, Purdue University.
Subject Area
Computer science
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