Our project seeks to introduce a novel function for determining the weight of each prover within the staking-based proving design of the Eldfell L3, Alpha-4 testnet.
Background
The Taiko team has been investigating multiple tokenomics strategies, including auto-adjustment of fees, auction-based, and stake-based models, in their quest for cost-efficient proofs. The auto-adjustment fee model has issues with prover redundancy, where multiple provers submit proofs for a single block. To ensure cost efficiency, it's clear that a designated prover must have the exclusive right to verify a block, thereby eliminating the issue of prover redundancy.
In their exploration, the Taiko team trialed a stake-based model to afford exclusive rights to TKO stakers. This model aims to promote cheaper proofs by adjusting the weight based on the inverse square of the proof reward. However, we believe the current weight function inevitably introduces vulnerabilities to discouragement attack (https://raw.githubusercontent.com/ethereum/research/master/papers/discouragement/discouragement.pdf). An attacker can artificially inflate their prover weight by setting a low expected reward per gas (R), enhancing their selection probability as a prover. This strategy could lead other provers to anticipate lower returns and subsequently exit the prover queue, enabling the attacker to dominate the prover market.
The discouragement attack can be proceeded in the following order:
1) Attacker sets his R as low as possible (75% * F) thereby making his weight bigger than any other provers.
2) Due to this high weight, the attacker can monopolize prover opportunities.
3) Regular provers, noticing the bias, may opt out of the prover queue since the attacker reaps most rewards.
4) With reduced competition, the attacker can then occupy the top 32 prover positions with their nodes, setting a high R value to maximize rewards.
Such an attacker would temporarily bear the attack cost, which might involve proving at rewards below their computational costs. However, the lure of gaining absolute control over the prover market may justify this short-term expense. Once in control, this dominant prover could exploit the network to their advantage.
Objectives
Our research aims to examine the susceptibilities present in current weight calculation functions concerning discouragement attacks. Further, we intend to devise a weight calculation function that stands resilient against such attacks.
Initially, our analysis will focus on gauging the potency of the discouragement attack. This will involve estimating both the cost and duration required for an attacker to execute the attack. Specifically, we'll determine the financial implications for the attacker to initiate the attack and assess the time it would take for them to dominate the proving market. These key metrics will serve as our foundation to evaluate the resilience of the weight calculation function against such attacks.
Next, drawing insights from the aforementioned metrics, we will develop a new weight calculation function. Our design will navigate a delicate balance: we aim to minimize the R value to prevent overcompensation of provers, while simultaneously ensuring the prover selection is resistant to discouragement attacks. Additionally, our approach will champion fairness, ensuring provers are given equitable opportunities provided they adhere to suitable reward parameters.
At the project's conclusion, we will produce a technical article delving into the analysis of prover selection methods with a focus on discouragement attacks. This article will also offer a comprehensive guideline, aiding in the selection of the best-suited solution for diverse scenarios.
Category
Alternative Proposer-Prover Tokenomics
Timeline
We expect 4-month timeline for the project.
Interval 1
Month 1: Research and Understanding
Begin a detailed vulnerability analysis of the current weight calculation functions.
Set up and run simulations to observe the discouragement attack's behavior with the existing model.
Month 2: Design and Initial Testing
Finalize the design for the new weight calculation function based on insights from Month 1.
Run simulations to observe the discouragement attack's behavior with the new model.
Interval 2
Month 3: Refinement and Comparative Analysis
Refine the weight calculation function based on the simulation outcomes from Month 2.
Compare the new model to the old one to assess the improvements.
Month 4: Finalization and Dissemination
Finalize the new weight calculation function.
Finalize and review the technical article.
Project Plan
Our project focuses on enhancing the weight calculation function used in staking based proving designs. The current method, as adopted by the Taiko team, is vulnerable to discouragement attacks. Our goal is to develop a new function resistant to these attacks, ensuring a stable and efficient proving system.
To accomplish our objective, our initial step involves conducting a thorough analysis of the current model's vulnerabilities using simulation. We will employ an agent-based environment for this purpose, enabling us to examine a range of discouragement attack scenarios in detail. Within this simulated framework, our focus will shift to the iterative process of designing, testing, and refining the weight calculation function to ensure its robustness against such attacks.
Once our current project is concluded, we aim to partner with the Taiko team to integrate our enhanced weight calculation function, ensuring it operates seamlessly in practical scenarios. Throughout this implementation phase, we will continuously monitor the function's performance in real-world conditions, making proactive recommendations for adjustments should unexpected challenges emerge.
Project Impact
Numerous methodologies have been proposed to optimize the prover incentive system. The staking-based proving design has emerged as a notably robust approach. However, to ensure the success of an L2 system, it's crucial to thoroughly analyze potential attack vectors.
The Ethereum community has extensively studied discouragement attacks on validators, especially in the context of its transition to a Proof-of-Stake (PoS) system. Similar types of attacks could compromise the integrity of a decentralized prover system, a component that is vital to the functioning of Taiko. Given this landscape, our project has the potential to make a significant contribution to Taiko by establishing a truly decentralized, zero-knowledge (zk) based Layer 2 solution that is both secure and scalable.
Team Information
Lano Technology, a web3 research and development company that specializes in DeFi mechanisms. Currently, we are collaborating with Alphanonce, a crypto native trading & investment firm in Asia, has +5 years of track record in crypto/web3 space with extensive DeFi experience. Aside from trading & investment, Alphanonce also contributes to Nansen, one of the biggest on-chain analytic platforms in the world, as an official Research Partner of Nansen and publishes reports to Xangle, the largest crypto data platform in Korea, which help Taiko improve awareness in Korea, one of the most activated crypto/web3 markets in the world.
Our team consists of three researchers and three software engineers. Our research team specializes in DeFi mechanisms, including the design of stablecoins, DEX mechanisms, the development of automated yield farming bots, and other protocols. Furthermore, our research team has strength in on-chain data analysis which is mostly done on Dune Analytics. Meanwhile, our development team is comprised of frontend/backend developers and designersFollowing are list of our team members.
Taeheon Lee: Senior researcher, M.S. in Computer Science
5 years of experience in quantitative research
Moonsoo Kim: Senior researcher, Ph.D. in Electrical Engineering
7 years of experience in quantitative research and simulation
Woosik Yoon: Researcher, B.S. in Mechanical Engineering
1 year of experience in blockchain research
May Jang: Researcher and technical writer, B.S. in Business
2 years of experience in blockchain research
Our previous experiences can be listed as follows. In the Relevant Links section, you can find some of our previous works.
In-depth investigation on stablecoins (Frax, MakerDAO, Terra, Liquity) and report publication. Design of stablecoin
Designing and operation of delta-neutral farming bots on Uniswap V3 pools
Experience in designing incentive schemes for L2 validators
Within the optimistic rollup framework, validators are tasked with uploading the output root of a block and raising challenges in case of discrepancies. Our incentive mechanisms prioritize encouraging validators to expedite both the upload and challenge processes. We believe our expertise in L2 validator incentive structures will enhance our understanding of Taiko's context and derive better project outcome.
Our team has conducted in-depth research on various stablecoins, specifically investigating the risks associated with each parameter of stablecoin design. You can find our previous analysis on stablecoins in the links below. Furthermore, following article written by us would help you understand our expertise:
In addition, to improve Taiko community awareness, Alphanonce team can leverage various research distribution channels such as Nansen ([website](https://www.nansen.ai/), one of the biggest on-chain analytic platforms in the world with +189k Twitter followers), Xangle ([website](https://xangle.io/en), the largest crypto data platform in Korea with +11k Telegram channel audiences), Twitter research [account](https://twitter.com/alphanonceStaff) (Alphanonce Internal with +6k followers, and Coinness ([website](https://coinness.com/), the largest crypot/web3 news platform in Korea with +40k DAU)
Agreement
[X] I agree to comply with the terms and conditions of the grants program
Project Description
Our project seeks to introduce a novel function for determining the weight of each prover within the staking-based proving design of the Eldfell L3, Alpha-4 testnet.
Background
The Taiko team has been investigating multiple tokenomics strategies, including auto-adjustment of fees, auction-based, and stake-based models, in their quest for cost-efficient proofs. The auto-adjustment fee model has issues with prover redundancy, where multiple provers submit proofs for a single block. To ensure cost efficiency, it's clear that a designated prover must have the exclusive right to verify a block, thereby eliminating the issue of prover redundancy.
In their exploration, the Taiko team trialed a stake-based model to afford exclusive rights to TKO stakers. This model aims to promote cheaper proofs by adjusting the weight based on the inverse square of the proof reward. However, we believe the current weight function inevitably introduces vulnerabilities to discouragement attack (https://raw.githubusercontent.com/ethereum/research/master/papers/discouragement/discouragement.pdf). An attacker can artificially inflate their prover weight by setting a low expected reward per gas (R), enhancing their selection probability as a prover. This strategy could lead other provers to anticipate lower returns and subsequently exit the prover queue, enabling the attacker to dominate the prover market.
The discouragement attack can be proceeded in the following order:
1) Attacker sets his R as low as possible (75% * F) thereby making his weight bigger than any other provers.
2) Due to this high weight, the attacker can monopolize prover opportunities.
3) Regular provers, noticing the bias, may opt out of the prover queue since the attacker reaps most rewards.
4) With reduced competition, the attacker can then occupy the top 32 prover positions with their nodes, setting a high R value to maximize rewards.
Such an attacker would temporarily bear the attack cost, which might involve proving at rewards below their computational costs. However, the lure of gaining absolute control over the prover market may justify this short-term expense. Once in control, this dominant prover could exploit the network to their advantage.
Objectives
Our research aims to examine the susceptibilities present in current weight calculation functions concerning discouragement attacks. Further, we intend to devise a weight calculation function that stands resilient against such attacks.
Initially, our analysis will focus on gauging the potency of the discouragement attack. This will involve estimating both the cost and duration required for an attacker to execute the attack. Specifically, we'll determine the financial implications for the attacker to initiate the attack and assess the time it would take for them to dominate the proving market. These key metrics will serve as our foundation to evaluate the resilience of the weight calculation function against such attacks.
Next, drawing insights from the aforementioned metrics, we will develop a new weight calculation function. Our design will navigate a delicate balance: we aim to minimize the R value to prevent overcompensation of provers, while simultaneously ensuring the prover selection is resistant to discouragement attacks. Additionally, our approach will champion fairness, ensuring provers are given equitable opportunities provided they adhere to suitable reward parameters.
At the project's conclusion, we will produce a technical article delving into the analysis of prover selection methods with a focus on discouragement attacks. This article will also offer a comprehensive guideline, aiding in the selection of the best-suited solution for diverse scenarios.
Category
Alternative Proposer-Prover Tokenomics
Timeline
We expect 4-month timeline for the project.
Interval 1
Interval 2
Project Plan
Our project focuses on enhancing the weight calculation function used in staking based proving designs. The current method, as adopted by the Taiko team, is vulnerable to discouragement attacks. Our goal is to develop a new function resistant to these attacks, ensuring a stable and efficient proving system.
To accomplish our objective, our initial step involves conducting a thorough analysis of the current model's vulnerabilities using simulation. We will employ an agent-based environment for this purpose, enabling us to examine a range of discouragement attack scenarios in detail. Within this simulated framework, our focus will shift to the iterative process of designing, testing, and refining the weight calculation function to ensure its robustness against such attacks.
Once our current project is concluded, we aim to partner with the Taiko team to integrate our enhanced weight calculation function, ensuring it operates seamlessly in practical scenarios. Throughout this implementation phase, we will continuously monitor the function's performance in real-world conditions, making proactive recommendations for adjustments should unexpected challenges emerge.
Project Impact
Numerous methodologies have been proposed to optimize the prover incentive system. The staking-based proving design has emerged as a notably robust approach. However, to ensure the success of an L2 system, it's crucial to thoroughly analyze potential attack vectors.
The Ethereum community has extensively studied discouragement attacks on validators, especially in the context of its transition to a Proof-of-Stake (PoS) system. Similar types of attacks could compromise the integrity of a decentralized prover system, a component that is vital to the functioning of Taiko. Given this landscape, our project has the potential to make a significant contribution to Taiko by establishing a truly decentralized, zero-knowledge (zk) based Layer 2 solution that is both secure and scalable.
Team Information
Lano Technology, a web3 research and development company that specializes in DeFi mechanisms. Currently, we are collaborating with Alphanonce, a crypto native trading & investment firm in Asia, has +5 years of track record in crypto/web3 space with extensive DeFi experience. Aside from trading & investment, Alphanonce also contributes to Nansen, one of the biggest on-chain analytic platforms in the world, as an official Research Partner of Nansen and publishes reports to Xangle, the largest crypto data platform in Korea, which help Taiko improve awareness in Korea, one of the most activated crypto/web3 markets in the world.
Our team consists of three researchers and three software engineers. Our research team specializes in DeFi mechanisms, including the design of stablecoins, DEX mechanisms, the development of automated yield farming bots, and other protocols. Furthermore, our research team has strength in on-chain data analysis which is mostly done on Dune Analytics. Meanwhile, our development team is comprised of frontend/backend developers and designersFollowing are list of our team members.
Taeheon Lee: Senior researcher, M.S. in Computer Science
Moonsoo Kim: Senior researcher, Ph.D. in Electrical Engineering
Woosik Yoon: Researcher, B.S. in Mechanical Engineering
May Jang: Researcher and technical writer, B.S. in Business
Our previous experiences can be listed as follows. In the Relevant Links section, you can find some of our previous works.
Point of Contact
tg:@thlee93 | email: research@lano.im, taeheon@lano.im
Previous Work
We provide a link to a page summarizing our previous works: https://lanotechnology.notion.site/Lano-Company-Profile-for-Web3-Clients-4d6741235dcc43908adcdfc60836fb5e?pvs=4.
Experience in designing incentive schemes for L2 validators
Within the optimistic rollup framework, validators are tasked with uploading the output root of a block and raising challenges in case of discrepancies. Our incentive mechanisms prioritize encouraging validators to expedite both the upload and challenge processes. We believe our expertise in L2 validator incentive structures will enhance our understanding of Taiko's context and derive better project outcome.
Experiences in creating dashboard
Our team has an experience in making dashboard for wide range of web3 projects (Liquity Chicken bond, OnePlanet, Layer 2). Among them, we received a Liquity grant for making dashboard for chicken bonds (https://dune.com/haechi_research/chicken-bonds-statistics, https://twitter.com/LiquityProtocol/status/1600882964123959296).
Expertise in stablecoins
Our team has conducted in-depth research on various stablecoins, specifically investigating the risks associated with each parameter of stablecoin design. You can find our previous analysis on stablecoins in the links below. Furthermore, following article written by us would help you understand our expertise:
https://stablelabs.substack.com/p/ecosystem-soundness-novel-criteria.
Profiles
Previous works
Partner teams
Alphanonce:
Additional Information
In addition, to improve Taiko community awareness, Alphanonce team can leverage various research distribution channels such as Nansen ([website](https://www.nansen.ai/), one of the biggest on-chain analytic platforms in the world with +189k Twitter followers), Xangle ([website](https://xangle.io/en), the largest crypto data platform in Korea with +11k Telegram channel audiences), Twitter research [account](https://twitter.com/alphanonceStaff) (Alphanonce Internal with +6k followers, and Coinness ([website](https://coinness.com/), the largest crypot/web3 news platform in Korea with +40k DAU)
Agreement