Abstract:
- The need for additional economic levers to protect DeFi against oracle integrity issues and better share common risks
- Oracle integrity is paramount to the health of DeFi
- Current consensus-based integrity can fail in extreme scenarios. DeFi protocols and their users should be assisted in such extreme events
Objective:
- Align incentives of data producers with consumers who are paying for the data consumed (despite the current close-to-zero pricing)
- Increase security of Pyth’s price feeds through an additional layer of economic security that tackles the risk of using Pyth’s service
Proposed Solution:
- the implementation of a staking mechanism for data publishers to protect users from the risks impacting data integrity
- prioritize the protection against the inaccuracy of the price data produced
- data producers should be awarded, proportionately to their stake, for continuous production of valuable and accurate data, yet they also need to be exposed to slashing risks, proportionately to their stake, in order to enhance the cryptoeconomic security of the service
- only applications that can show on-chain proof for payments for Pyth’s data service should be eligible for any loss protection
Example:
- If the Pyth price deviates from a reasonable level that is representative of the market conditions for liquidity and recent executions, resulting in unwarranted liquidations, the publishers for such price aggregate should be investigated by the DAO and slashing should be actioned in case evidence is deemed compelling from the community
- Such applicable events need clear definition and means to observe and measure
- Everyone staking should share in the economics of the pool
- Slashing can be socialized between publishers of the impacted feed(s)
- the amount of slashing should be calibrated for the amounts that DeFi protocols have been impacted for and the number of such protocols
- All amounts should be calculated in $PYTH
Questions and Uncertainties:
- Amounts of rewards and penalties, and criteria for loss protection are uncertain and require further design
- Addressing the risk of inaccurate data should involve stakeholders outside of data publishers. it is unclear if and how $PYTH holders could participate in staking