1 - Describe the difference between GRIN’s and BEAM’s…
Funding - Grin followed a highly principled, cypherpunk ideology – including no token premine or ICO, as well as volunteer-based development
- Grin is structured as a research project. They do not receive any outside funding except for donations, they do it part-time.
– Beam sought VC funding and hired a team of developers to work on the software full-time, allowing it to speed ahead of Grin in its implementation.
Governance - Beam takes its example from privacy-centric cryptocurrency zcash, maintaining a corporate structure, and funneling a portion of the block reward into a Foundation to support the blockchain’s development.
- Beam is a privacy coin an alignment of incentives within the block rewards so that the project stays alive.
- Grin: As not engage in any ICO, pre-mine or founder rewards, Grin uses a less reliable income source by relying on a community funding model that is similar to the one utilized by the monero project.
Target Customer - Beam focuses on usability by having a simple wallet interface that is considered central to the project’s overall value-add.
A GUI wallet and a mobile wallet will increase adoption, increase number of transactions and usage and will thus increase the anonymity set.
Also, wallets boast implementation in different operating systems, including MacOS, Windows, and Linux.
- Grin is very much aimed at a technical crowd, and will be very much ‘use at your own risk’.
- Grin currently only offers a command-line wallet, and is less accessible for non-technical users.
Emission Schedules - Beam sees itself as a “store of value” coin that has a fixed issuance schedule akin to bitcoin.
Grin’s monetary policy is unfixed. At present, a new token is issued every second. This is due to the project’s belief that sustained issuance will stabilize the value of the currency.
2 - What is the key privacy concern of both, and what feature do they implement to deal with this?
Both implementations have a concern that they may be potentially vulnerable to machine-learning analysis – due to the design’s failure to conceal inputs and outputs
Both teams currently implement a privacy feature named Dandelion to better conceal these potential leaks, there may be other experimental efforts that can be concluded as well going forward.