Performance predictability, ensuring low latency variability, is crucial for the reliability and efficiency of blockchain consensus. Byzantine Fault Tolerant Proof-of-Stake (BFT-PoS) consensus aims to achieve stable transaction processing latency by scheduling block generation at consistent intervals. However, BFT-PoS's incentive mechanisms grant all transaction tips to the block proposer, which can be exploited by delaying proposals to gain extra Maximal Extractable Value (MEV) rewards, thus undermining performance predictability. Existing solutions impose penalties for delays but lack a standard for measuring the extra rewards from delays or fail in malicious environments. This paper introduces InTime, a novel approach to safeguard performance predictability in BFT-PoS by economically motivating timely block proposals. We first introduce the untimely MEV ratio , a reliable metric to measure the extra rewards gained from proposal delays, facilitating our countermeasures against deliberate delays. Furthermore, we propose the arrival rate incentive (ARI), aligning rewards with transaction arrival timing among nodes to reduce potential MEV manipulation. To make ARI robust against malicious behaviors, we establish a committee time witness (CTW) workflow to accurately gather and verify transaction arrival times. Extensive experiments demonstrate that InTime can effectively reduce latency variability by up to 95.9%.

Blockchains have gained wide adoption for secure data processing. As blockchain data volumes grow, the demand for efficient data analysis, especially aggregate queries, becomes increasingly critical. However, current blockchains lack native support for efficient analytical query processing, forcing users to either maintain full replicas or rely on third-party services without integrity guarantees. In this paper, we propose an efficient framework, Merkle Bloom Filter Tree (MBFT), for authenticated aggregate queries that combine boolean keywords and range predicates on blockchains. At its core is a Bloom filter-based authenticated data structure that supports both types of predicates, constructed per block for efficient transaction indexing. For temporal predicates, we optimize time window queries through value pruning and block consolidation. We design a novel Merge Bloom Filter (MBF) for space-efficient handling of dynamic sets during query authentication. We provide a theoretical analysis of the storage overhead caused by the Bloom filter's false positive rates. Our framework employs data sketches to support various aggregate operations. Extensive experiments demonstrate that MBFT has improved the query speed by up to 286× compared to state-of-the-art authenticated query solutions.

The popularity of decentralized finance has drawn attention to liquidity mining (LM). In LM, a user deposits her cryptocurrencies into liquidity pools to provide liquidity for exchanges and earn yields. Different liquidity pools offer varying yields and require different pairs of cryptocurrencies. A user can exchange a cryptocurrency for another with some exchange costs. Thus, an LM solution consists of exchange transactions and deposit transactions, guaranteeing (1) each exchange transaction must exchange one cryptocurrency for another at a specific rate (i.e., the exchange constraint); (2) the amounts of cryptocurrencies deposited in a liquidity pool must exceed the required threshold (i.e., the minimum constraint); (3) each deposit transaction must deposit a specific pair of cryptocurrencies at a certain rate in a liquidity pool (i.e., the deposit constraint); and (4) the cryptocurrencies used in the solution do not exceed the cryptocurrencies that the user has (i.e., the budget constraint). Selecting the most profitable LM solution is challenging due to the vast number of candidate solutions. To address this challenge, we define the yield maximization liquidity mining (YMLM) problem. Given a set of liquidity pools, a set of the user's cryptocurrencies, a set of exchange rates, and an evaluation function, YMLM aims to find an LM solution with maximal yields, satisfying the minimum, exchange, deposit, and budget constraints. We prove that YMLM is NP-hard and cannot be solved by algorithms with constant approximation ratios. To tackle YMLM, we propose two algorithms, namely YMLM\_GD and YMLM\_SK, with parameterized approximation ratios. Extensive experiments on both real and synthetic datasets show that our approaches outperform the baselines in yields.

The proof-of-work (PoW) based blockchains are more secure nowadays since profit-oriented miners contribute more computing powers in exchange for fair revenues. This virtuous circle only works under an incentive-compatible consensus, which is found to be fragile under selfish mining attacks. Specifically, selfish miners can conceal and reveal blocks strategically to earn unfairly higher revenue compared to honest behaviors. Previous countermeasures either require incompatible modifications or fail to consider the asynchronous network and multiple honest nodes setting in reality. In this paper, we introduce the unfairness measurement based on the KL-divergence from the computing power distribution to the revenue distribution of miners. To improve fairness with the existence of selfish miners, we propose a novel block promotion strategy namely Tit-for-Tat (TFT), for honest miners. In particular, based on a miner's local observation of forks, we design the suspicious probability measurement of other nodes. Rather than promoting a fresh block instantly, miners withhold it for different time periods according to others' suspicious probability before delivery. Meanwhile, to minimize the attacker's unfair revenue, we formulate the delay vector (DV) problem for honest miners to determine the optimal withholding time. We prove that DV problem is nonconvex, and thus propose two approximation algorithms that yield ϵ-suboptimal solutions. In addition, we extend TFT strategy to support dynamic networks. Extensive experiments validate the efficiency and effectiveness of our strategy and algorithms to reduce unfairness by 54.62% within bounded withholding time.