PROP overstates T-PAW profit, EXP understates it; PAH yields high gains on PROP
The claim
GRP misestimates T-PAW revenue and PAH can greatly increase adversary profit on PROP pools
Published as: “Power Adjusting Games Under Block-Dependent Pool Payout Schemes”
So what
Pool operators can reduce vulnerability to withholding and hopping attacks by preferring PPLNS over PROP or EXP, and by tuning the EXP decay rate to balance the trade-off.
The five-minute version
The paper finds that the commonly used global-reset PROP (GRP) misestimates the profit of advanced block-withholding attacks: it overstates T-PAW gains under PROP by up to an order of magnitude and understates them under EXP, while PPLNS is the least vulnerable scheme. It analyzes T-PAW under the real-world payout schemes PPLNS, round-based PROP and EXP, introduces a new Power Adjusting Hopping (PAH) attack, and derives closed-form revenue ratios that are validated by Monte Carlo simulations of 10^7 attack cycles.
What they found 2
- GRP misestimates the T-PAW gain: it overstates the gain under PROP and understates it under EXP.
“GRP misestimates the T-PAW gain: it overstates the gain under PROP up to an order of magnitude and understates it under EXP.”
- PROP is highly vulnerable to PAH, while EXP faces a trade-off between PAH and T-PAW depending on the decay rate.
“PROP is highly vulnerable to PAH, while EXP faces a trade-off: a slow decay invites PAH and a fast decay invites T-PAW.”
What undercuts it the paper’s own results
- the T-PAW gain is of the same order as the confidence interval for PPLNS and PROP, implying these RER values alone are not a sharp test.
What’s new here 4
- First closed-form analysis of T-PAW under practical block-dependent payout schemes (PPLNS, PROP, EXP) instead of the unrealistic GRP.
- Introduction of the Power Adjusting Hopping (PAH) attack that adjusts hash power without withholding blocks.
- Derivation of revenue ratios using renewal arguments for PPLNS and stationary carry processes for PROP and EXP.
- Demonstration that GRP misestimates T-PAW gains and that each payout scheme is vulnerable to a different attack.
Artifacts & methods 5
- T-PAW attack analysistheory
- Power Adjusting Hopping (PAH) attacktheory
- Monte Carlo simulation frameworkevaluation
- Renewal argument for PPLNStheory
- Stationary carry process for PROP and EXPtheory