Human society is transitioning to a knowledge economy. The rise of Artificial Intelligence has once more shown that the value of data grows dramatically as they are shared and aggregated.However, from established applications like Finance, which require selective handling of confidential information, to the AI revolution - the same issue persists: we share data with centralized entities with no guarantees on the exact handling of our data. We propose a system (Palliora) based on a public blockchain in which everyone can selectively share their data for trusted, confidential and verifiable computation, knowing that only the intended recipients will have access. Moreover, such a system guarantees fairness to all participants in an AI ecosystem: data providers, model providers, training providers, model users, which is essential for an unplugged growth of AI technology. We achieve our goals by introducing in Palliora both several independent actors, that populate overlapping networks, and a peculiar contract standardization. Among our actors we have DA nodes, which take care of Data Availability, Guardians, which take care of a decentralized access-control system, Calculators, which perform outsourced computations (with varying confidentiality levels), and Verifiers, which check the quality and consistency of input and output data. Palliora’s contracts permit free negotiations among all users and all actors, while enforcing correct execution and fair distribution of rewards.
Dynamically distributed inflation is a common mechanism used to guide a blockchain’s staking rate towards a desired equilibrium between network security and token liquidity. However, the high sensitivity of the annual percentage yield to changes in the staking rate, coupled with the inherent feedback delays in staker responses, can induce undesirable oscillations around this equilibrium. This paper investigates this instability phenomenon. We analyze the dynamics of inflation-based reward systems and propose a novel distribution model designed to stabilize the staking rate. Our solution effectively dampens oscillations, stabilizing the yield within a target staking range.
We present our work-in-progress approach to computable contracts, where all roles in a computation may be outsourced, from the servers performing computations, to those providing input, to those performing verifications (on input and on output), including all related communications. Varying levels of confidentiality can be chosen on both data and calculations. Although the largest part of the computational and communication effort is performed off-chain, our contracts require a specialized underlying blockchain where they are encoded as transactions. This enables decentralized handling and enforces correct execution through a combination of cryptographic techniques and economic security. Our delegation architecture allows for the execution of very complex collaborative tasks, such as decentralized AI.
Privacy is one of the essential pillars for the widespread adoption of blockchains, but public blockchains are transparent by nature. Modern analytics techniques can easily subdue the pseudonymity feature of a blockchain user. Some applications have been able to provide practical privacy protections using privacy-preserving cryptography techniques. However, malicious actors have abused them illicitly, discouraging honest actors from using privacy-preserving applications as mixing user interactions and funds with anonymous bad actors, causing compliance and regulatory concerns. In this paper, we propose a framework that balances privacy-preserving features by establishing a regulatory and compliant framework called Selective De-Anonymization (SeDe). The adoption of this framework allows privacy-preserving applications on blockchains to de-anonymize illicit transactions by recursive traversal of subgraphs of linked transactions. Our technique achieves this without leaving de-anonymization decisions or control in the hands of a single entity but distributing it among multiple entities while holding them accountable for their respective actions. To instantiate, our framework uses threshold encryption schemes and Zero-Knowledge Proofs (ZKPs).
We propose a middleware solution designed to facilitate seamless integration of privacy using zero-knowledge proofs within various multi-chain protocols, encompassing domains such as DeFi, gaming, social networks, DAOs, e-commerce, and the metaverse. Our design achieves two divergent goals. zkFi aims to preserve consumer privacy while achieving regulation compliance through zero-knowledge proofs. These ends are simultaneously achievable. zkFi protocol is designed to function as a plug-and-play solution, offering developers the flexibility to handle transactional assets while abstracting away the complexities associated with zero-knowledge proofs. Notably, specific expertise in zero-knowledge proofs (ZKP) is optional, attributed to zkFi's modular approach and software development kit (SDK) availability. .
Executing on decentralized exchanges (DEXs) provides a higher level of security for clients’ funds. Clients can execute their trades directly from their own wallet, using smart contracts, and keep custody of their assets. This higher level of security comes at the cost of “gas fees”, which users of a blockchain have to pay to validators for verifying transactions. In this paper, we analyze the effect of gas fees and network speed on execution cost and liquidity distribution on the largest DEX, Uniswap v3. Specifically, we use the entry of Polygon, a scaling solution to the incumbent Ethereum, as an exogenous shock to gas fees reduction and speed increase. We expect that lower gas fees and higher speed on Polygon should lead to higher concentration of liquidity around the market price. Indeed, it becomes easier for liquidity providers to revise their positions and re-post liquidity around the market price. Our preliminary findings show that, whereas overall market depth on Polygon is lower compared to Ethereum, liquidity is indeed more concentrated around the market price. This higher liquidity concentration is especially important for execution of smaller trades. Indeed, we find that price impact for smaller trades (up to $10K) is lower on Polygon, compared to Ethereum.
On November 22nd 2022, the lending platform AAVE v2 (on Ethereum) incurred bad debt resulting from a major liquidation event involving a single user who had borrowed close to $40M of CRV tokens using USDC as collateral. This incident has prompted the Aave community to consider changes to its liquidation threshold, and limitations on the number of illiquid coins that can be borrowed on the platform. In this paper, we argue that the bad debt incurred by AAVE was not due to excess volatility in CRV/USDC price activity on that day, but rather a fundamental flaw in the liquidation logic which triggered a toxic liquidation spiral on the platform. We note that this flaw, which is shared by a number of major DeFi lending markets, can be easily overcome with simple changes to the incentives driving liquidations. We claim that halting all liquidations once a user's loan-to-value (LTV) ratio surpasses a certain threshold value can prevent future toxic liquidation spirals and offer substantial improvement in the bad debt that a lending market can expect to incur. Furthermore, we strongly argue that protocols should enact dynamic liquidation incentives and closing factor policies moving forward for optimal management of protocol risk.
Using the devaluation of the TerraUSD peg as a case study, this column shows how algorithmic stablecoins are vulnerable to speculative attacks when the system is under-collateralised. The authors point to solutions – stable collateral and over-collateralisation – to stabilise the peg.
We assess the market risk of the lending protocol using a multi-asset agent-based model to simulate ensembles of users subject to price-driven liquidation risk. Our multi-asset methodology shows that the protocol’s systemic risk is small under stress and that enough collateral is always present to underwrite active loans.