In this paper, we present the full design of CoinPrune, our block-pruning protocol that is retrofittable to Bitcoin via a velvet fork.
This work extends our initial paper presented at IFIP Networking 2020 by enabling CoinPrune to obfuscate most objectionable content stored in the UTXO set and introducing an additional store for application-level data.
Furthermore, we extend our security discussion, our discussion of related work, we updated our performance evaluation, and we released a prototype implementation of CoinPrune.
This paper presents CoinPrune, a protocol for block pruning that is fully compatible to Bitcoin and therefore allows for gradual deployment.
CoinPrune allows joining nodes to bootstrap using a state, which has been advertised on the blockchain recently, instead of having to download and verify all blockchain data.
We maintain Bitcoin compatiblity by implementing CoinPrune as a velvet fork, i.e., instead of rejecting invalid state advertisements, we solely rely on positive state reaffirmations by multiple miners.
Our evaluation shows users can reduce their synchronization times from 5 hours to 46 minutes using CoinPrune, while downloading only 5 GiB instead of 230 GiB of blockchain data as of October 2019.
Processes in the insurance economy are often cumbersome and expensive because of the inherently opposing interests of insurers and customers.
Smart contracts bear a large potential to simplify these processes and thereby reduce costs.
In this paper, …
Since the insertion of illicit content into public blockchains can have severe consequences for users, we explore and discuss the design space for preventing this insertion.
Our findings show that firewall-like scanning of Bitcoin transactions poses no viable solution to the problem.
However, while content insertion cannot entirely be prevented by technical means, self-verifying blockchain identifiers make currently simple manipulations hard.
Finally, the introduction of mandatory minimum fees can be used to disincentivize content insertion additionally.