This paper studies enhancement of training data D to improve the robustness of machine learning (ML) classifiers M against adversarial attacks on relational data. Data enhancing aims to (a) defuse poisoned imperceptible features embedded in D , and (b) defend against attacks at prediction time that are unseen in D . We show that while there exists an inherent tradeoff between the accuracy and robustness of M in case (b), data enhancing can improve both the accuracy and robustness at the same time in case (a). We formulate two data enhancing problems accordingly, and show that both problems are intractable.Despite the hardness, we propose a framework that integrates model training and data enhancing. Moreover, we develop algorithms for (a) detecting and debugging corrupted imperceptible features in training data, and (b) selecting and adding adversarial examples to training data to defend against unseen attacks at prediction time. Using real-life datasets, we empirically verify that the method is at least 20.4% more robust and 2.02X faster than SOTA methods for classifiers M , without degrading the accuracy of M .

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 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.

With the rapid development of blockchain, the concept of decentralized applications (DApps), built upon smart contracts, has attracted much attention in academia and industry. However, significant issues w.r.t. system throughput, transaction confidentiality, and the security guarantee of the DApp transaction execution and order correctness hinder the border adoption of blockchain DApps. To address these issues, we propose L2chain, a novel blockchain framework aiming to scale the system through a layer-2 network where DApps process transactions in the layer-2 network and only the system state digest, acting as the state integrity proof, is maintained on-chain. To achieve high performance, we introduce the split-execute-merge (SEM) transaction processing workflow with the help of the RSA accumulator, allowing DApps to lock and update a part of the state digest in parallel. We also design a witness cache mechanism for DApp executors to reduce the transaction processing latency. To fulfill confidentiality, we leverage the trusted execution environment (TEE) for DApps to execute encrypted transactions off-chain. To ensure transaction execution and order correctness, we propose a two-step execution process for DApps to prevent attacks ( i.e. , rollback attacks) from subverting the state transition. Extensive experiments have demonstrated that L2chain can achieve 1.5X to 42.2X and 7.1X to 8.9X throughput improvements in permissioned and permissionless settings respectively.

Zero-knowledge set membership (ZKSM) proof is widely used in blockchain to enable private membership attestation. However, existing mechanisms do not fully consider dynamic issues in the blockchain scenario. Particularly, frequent addition/removal of set elements, not only brings the significant cost to keep public parameters up to date to provers and verifiers but also affects mechanism efficiency (e.g., generation time of the proof and verification, etc.). In this paper, we propose DIV to shard elements on blockchain into independent subsets with the same cardinality to reduce the effect of dynamic issues. However, due to the diverse proof frequency, an improper element-set assignment can result in frequently used elements being easily inferred and corrupted. Thus, we formalize the assignment problem under both element addition and removal cases as two optimization problems and prove their NP-hardness. For each problem, we consider two cases if each element proof frequency is known in advance by the set maintainer or not, and propose solutions with theoretical guarantees. We implement DIV on both Merkle tree and RSA-based ZKSM mechanisms to evaluate its efficiency and effectiveness and apply DIV on a ZKSMbased application named zkSync to demonstrate its applicability. Results show that DIV can achieve O(1) time/space cost on ZKSM under dynamic situations while protecting the information about frequently used elements. It also notably reduces the system latency of zkSync.

Recently, crowdsourcing has emerged as a new computing paradigm to solve problems that need human intrinsic, such as image annotation. However, there are two limitations in existing crowdsourcing platforms, i.e. non-transparent incentive mechanism and isolated profiles of workers, which harms the interests of both requesters and workers. Meanwhile, Blockchain technology introduces a solution to build a transparent, immutable data model in the Byzantine environment. Moreover, Blockchain systems (e.g. Ethereum) can also support the Tuning-complete script called smart contracts. Thus, we are motivated to use the feature of the transparent data model and smart contract in Blockchain to address the two limitations. Based on the proposed solutions, we have designed a Blockchain based framework which supports foundations of general crowdsourcing platforms. In addition, our framework also has following novel features: (1) it provides the transparent incentive mechanisms; (2) it supports a trusted worker's profile sharing in a cross-platform mode.

With the success of Bitcoin, the technique behind it, Blockchain, is catching massive attention recently. Blockchain is a collection of several techniques like cryptology, P2P and distributed consensus protocol. The main idea of Blockchain is that nodes in the network keep the same distributed ledger. Because of this immutable ledger, a trusted bridge is built among parties without fully trust. Blockchain can be used in variety of areas, especially in financial fields, like supply chain management, cross-border payment and global bank settlement. Meanwhile, we can observe that mobile network is growing rapidly and nibbling the PC market. However, the current public Blockchain applications like Bitcoin or Ethereum require the nodes to store the whole ledger which exceeds the capacity of the mobile devices. Thus, we need to develop a blockchain platform to support mobile devices. In this demo, we introduce Jupiter, a mobile-based Blockchain platform which provides a novel concept called consensus unit (CU) to alleviate the storage problem of mobile. We present the system architecture and demonstrate several CU scenarios via Jupiter.

Recently, Blockchain becomes a hot research topic due to the success of Blockchain in many applications, such as cryptocurrency, smart contract, digital assets, distributed cloud storage and so on. The power of Blockchain is that it can achieve the consensus of an ordered set of transactions among nodes which do not trust each other, even with the existence of malicious nodes. However, compared to traditional databases, the current Blockchain technology still cannot handle a massive number of transactions, which is caused by many factors, such as the consensus protocol, structure of the blocks and storage challenge. Among them, the high storage requirement is a key factor that prevents the wide usage of Blockchain on various devices such as mobile phones or low-end PCs. In this paper, to address the storage challenge, we introduce a novel concept called Consensus Unit (CU), which organizes different nodes into one unit and lets them to store at least one copy of Blockchain data in the system together. Based on this idea, we further define the Blocks Assignment Optimization (BAO) problem which determines the optimal assignment of blocks such that the storage space is fully used and the query cost is minimized. We prove that the BAO problem is NP-hard. Thus, we propose three efficient heuristic algorithms to solve the static assignment problem. Furthermore, we present solutions to address the dynamic scenarios when new blocks arrive and nodes join or depart from the CU. To verify the effectiveness of CU, we have conducted extensive experiments on synthetic data and BLOCKBENCH [1]. The results have confirmed the superiority of CU in saving the storage and maintaining the system throughput.