Broadcast privacy for blockchains

Abstract

Public blockchains have reached high popularity among technically inclined people, laypersons and researchers alike. Similarly, privacy has gained much attention in the same circles. This attention and high sensitivity of information transmitted in blockchains, lead more and more blockchain-based systems, especially cryptocurrencies, to provide privacy for their users. Popular approaches include ring signatures or zero-knowledge proofs to achieve unlinkable payments within the blockchain.

However, these systems solely examine privacy by considering the blockchain and its embedded transactions. The underlying peer-to-peer network of a public blockchain is rarely considered. This leaves the dissemination of transactions open for privacy attacks, as the IP address of the originator of a transaction can be mapped to their real-world identity.

In this thesis, we look into the important privacy aspects of broadcasting blockchain transactions. We collect and analyse data of a large blockchain network and construct a privacy-preserving latency estimator. Building on the insights gained from the analysis, we combine dining-cryptographers networks with a secret sharing technique and layer a flood-and-prune broadcast on top, to provide enforced k-anonymity to network participants.

To increase the flexibility of this approach, we extend two established privacy protocols. First, we extend a dining-cryptographers based group messaging protocol to transmit arbitrary length messages. Further, we optimize the protocol for common cases, to improve its performance for various environments, especially blockchain transaction dissemination. As a performant intermediate privacy layer, we transform adaptive diffusion from a contact graph protocol to a computer network protocol. We achieve this by changing the underlying network assumptions and the attacker model. We derive optimal forwarding probabilities based on a statistical network model of unstructured peer-to-peer networks. These two sub-protocols are combined in an intertwined layering approach to create 3P3, a flexible privacy-preserving broadcast protocol.

Lastly, to manage the groups required for 3P3 and other proposed and common protocols, we propose Pixy. Pixy is a privacy increasing group creation scheme, allowing for filtering and testing of group participants to establish trust. The system allows for smaller group sizes while maintaining privacy guarantees of previous systems, or better privacy for same-sized groups.

The software, concepts, data and models in this thesis help researchers and developers of privacy preserving network protocols. Developers can use 3P3, tuning its parameters to the needs of their network. Researchers can build on the data, concepts and models to create novel schemes and generalizations of our insights. This improves privacy for all users of modern and future networks.