Bipartite graphs are widely used to model relationships between entities of different types, where vertices are divided into two disjoint sets. Biclique-preserving clustering is a fundamental operation that retrieves clusters with dense bicliques, enabling various emerging applications. However, existing methods either fail to accurately capture the unique properties of bipartite graphs or significantly overlook the informative higher-order biclique substructure, leading to compromised clustering quality. Additionally, existing methods are overly dependent on biclique enumeration, resulting in poor scalability. To address these challenges, we propose ECRC, a simple yet provable Edge-Centric Reweighting Clustering framework that provides strict approximation guarantees for any biclique. A key advantage of ECRC is its ability to leverage powerful counting instead of exhaustive enumeration, significantly reducing time and space complexity. To further improve efficiency, we propose several effective graph reduction strategies to eliminate the unqualified vertices and edges before calculating the edge-centric weight. Extensive experiments on five datasets show that our algorithms are more efficient and effective compared to six baselines.

Machine learning (ML) models are widely deployed on edge nodes, such as mobile phones and edge servers, to power a wide range of AI applications over the web. Ensuring the integrity of these edge models is paramount, as they are subject to corruption caused by software/hardware exceptions and malicious tampering, which may undermine model performance, incur economic losses, and pose health risks. Existing data integrity mechanisms designed for files stored on disks cannot properly verify the integrity of models running in GPUs or mitigate the new integrity threats against edge models. This paper proposes EdgeThemis, a novel mechanism for verifying the integrity of edge models through sentinel verification. To enable verifiability for a model M, EdgeThemis embeds a sentinel backdoor and a verification module into M. Then, a challenger can send verification requests to the edge node hosting M to verify its integrity. Next, the sentinel activates the verification module to generate a unique integrity proof tied to the identity of the edge node for verification. Finally, the challenger can verify the integrity proof to detect model corruption. Theoretical analysis proves that EdgeThemis can properly mitigate potential integrity threats against edge models. Experiments demonstrate that EdgeThemis achieves a verification accuracy of 100.00% across various models and different types of model corruption with robustness against replay attacks, theft attacks, and replacement attacks.

Asynchronous Byzantine Fault Tolerant (BFT) consensus protocols have garnered significant attention with the rise of blockchain technology. A typical asynchronous protocol is designed by executing sequential instances of the Asynchronous Common Sub-seQuence (ACSQ). The ACSQ protocol consists of two primary components: the Asynchronous Common Subset (ACS) protocol and a block sorting mechanism, with the ACS protocol comprising two stages: broadcast and agreement. However, current protocols encounter three critical issues: high latency arising from the execution of the agreement stage, latency instability due to the integral-sorting mechanism, and reduced throughput caused by block discarding. To address these issues, we propose Falcon, an asynchronous BFT protocol that achieves low latency and enhanced throughput. Falcon introduces a novel broadcast protocol, Graded Broadcast (GBC), which enables a block to be included in the ACS set directly, bypassing the agreement stage and thereby reducing latency. To ensure safety, Falcon incorporates a new binary agreement protocol called Asymmetrical Asynchronous Binary Agreement (AABA), designed to complement GBC. Additionally, Falcon employs a partial-sorting mechanism, allowing continuous rather than simultaneous block committing, enhancing latency stability. Finally, we incorporate an agreement trigger that, before its activation, enables nodes to wait for more blocks to be delivered and committed, thereby boosting throughput. We conduct a series of experiments to evaluate Falcon, demonstrating its superior performance.

AsynchronousByzantine Fault Tolerance(BFT) consensus protocols are gaining attention for their resilience against network attacks. Among them,Multi-valued Byzantine Agreement(MVBA) protocols play a critical role, which accepts input values from each replica and returns a consistent output. The state-of-the-art MVBA protocol, sMVBA, has a good-case latency of$6\delta$and an expected bad-case latency of$12\delta$, with$\delta$representing the network delay. Additionally, sMVBA exhibits a communication of$O(n^{2})$in both good and bad cases. Although it outperforms other MVBA protocols, sMVBA still lags behind partially-synchronous counterparts. For instance, PBFT achieves a good-case latency of$3\delta$, and HotStuff boasts a good-case communication of$O(n)$. This paper introduces a novel MVBA protocol, Pako, aiming for performance comparable to partially-synchronous protocols. Pako leverages an existing MVBA protocol as a black box and introduces an additional view with an optimistic path to commit values efficiently. Two Pako variants, Pako1 and Pako2, provide a trade-off between latency and communication. To be more specific, Pako1 achieves a good-case latency of$3\delta$with$O(n^{2})$communication, while Pako2 reduces the communication to$O(n)$with a slightly higher good-case latency of$5\delta$. A series of experiments demonstrate Pako's significant outperformance of counterparts.

Standing as a foundational element within blockchain systems, theByzantine Fault Tolerant(BFT) consensus has garnered significant attention over the past decade. The introduction of aDirected Acyclic Directed(DAG) structure into BFT consensus design, termed DAG-based BFT, has emerged to bolster throughput. However, prevalent DAG-based protocols grapple with substantial latency issues, suffering from a latency gap compared to non-DAG protocols. For instance, leading-edge DAG-based protocols named GradedDAG and BullShark exhibit a good-case latency of$4$and$6$communication rounds, respectively. In contrast, the non-DAG protocol, exemplified by PBFT, attains a latency of$3$rounds in favorable conditions. To bridge this latency gap, we propose Remora, a novel DAG-based BFT protocol. Remora achieves a reduced latency of$3$rounds by incorporating optimistic paths. At its core, Remora endeavors to commit blocks through the optimistic path initially, facilitating low latency in favorable situations. Conversely, in unfavorable scenarios, Remora seamlessly transitions to a pessimistic path to ensure liveness. Various experiments validate Remora's feasibility and efficiency, highlighting its potential as a robust solution in the realm of BFT consensus protocols.

Sandwich arbitrage is a popular strategy for blockchain-based decentralized exchange (DEX) users to facilitate decentralized token exchanges and make profits. However, conventional Sandwich arbitrage only focuses on one big-deal transaction and executes arbitrary actions within an intrablock, leading to a huge loss of profit opportunities. In this article, we present Lasagne, a novel high-profit arbitrage strategy that allows the exploration of profit opportunities from multiple small-deal transactions across interblocks. Specifically, Lasagne involves a suite of novel strategies: fishing, bundle, and grid, each of which is tailored to a liquidity pool with diverse transaction frequencies and skewness. The experimental results demonstrate the superiority of Lasagne over a state-of-the-art sandwich in terms of feasibility and effectiveness.

Among various blockchain applications, decentralized anonymous payment (DAP) systems stand out for their enhanced privacy protection compared to traditional payment methods. However, DAPs face challenges such as the lack of asset recovery and identity verification features. To ensure the long-term healthy development of DAP systems, adherence to legal regulations and privacy protection is equally critical. In response to these requirements, we propose a$\textsf {CR}$-$\textsf {DAP}$system that offers a secure and efficient solution without compromising on practicality. Our innovation lies in introducing an identity-based traceable anonymous signature scheme, which skillfully balances anonymity with traceability. This scheme supports private key retrieval and allows for identity tracking when necessary, addressing key pain points in existing anonymous payment systems and enhancing user trust. We have implemented the prototype of this signature scheme and the$\textsf {CR}$-$\textsf {DAP}$system, evaluating its performance to demonstrate its practicality.

Broadcast primitives likeReliable Broadcast(RBC) are integral toDirected Acyclic Graph(DAG)-based asynchronousByzantine Fault Tolerant(BFT) protocols. Despite recent advancements, these protocols often suffer from high latency due to the inherent three communication rounds in RBC. To mitigate this latency, we propose employingBest-Effort Broadcast(BBC) for message dissemination, which requires only one communication round. However, leveraging BBC poses challenges in constructing a DAG-based ledger and ensuring consistent commitment in the face of contradictory blocks. In this paper, we introduce PlainDAG, a low-latency and secure asynchronous DAG-based BFT protocol that eschews complex broadcast primitives. Instead, it achieves lower latency by simplifying broadcast and committing with a larger quorum of votes. Our approach addresses the integrity, agreement, and totality properties lacking in BBC compared to RBC, thus ensuring correctness. Key to the design of PlainDAG is the introduction of anIntegral Referencefield in blocks, which references previously committed blocks and facilitates voting among contradictory blocks. Additionally, we devise a block query scheme to retrieve missing blocks. Theoretical analysis demonstrates the generalized design applicability of PlainDAG for asynchronous DAG-based BFTs, with the best-case latency of 2 communication rounds and practical resilience. Experimental results underscore the superiority of PlainDAG over state-of-the-art asynchronous DAG-based protocols in latency, while achieving comparable throughput performance.

In the mobile edge computing environment, caching data in edge storage systems can significantly reduce data retrieval latency for users while saving the costs incurred by cloud-edge data transmissions for app vendors. Existingedge data caching(EDC) methods prioritize popular data and aim to minimize users’ data retrieval latency and system storage costs jointly. However, these EDC methods often rely on the assumption that data popularity always follows certain distributions. As a result, they cannot properly adapt to the fluctuations in data popularity due to user mobility or unexpected increases in user demands. Meanwhile, unlike cloud data centers, complex and fragile edge servers are more likely to experience physical failures or network outages, presenting new challenges for EDC strategies. Specifically, when an edge server fails or experiences an outage, cached data may become temporarily unavailable, leading to increased latency as requests are redirected to alternative servers or the cloud. In this paper, to enableuncertainty-aware edge data caching(uEDC), we first model the problem as a robust optimization problem and propose an optimal algorithm named uEDC-B to find the optimal uEDC solution. To address the high computational complexity of uEDC-B, we introduce an approximate algorithm named uEDC-L based on linear decision rules. Theoretical analysis and extensive experiments on a real-world dataset demonstrate that the proposed methods outperform two state-of-the-art approaches in handling the uncertainties in data popularity and edge server failure with a significant performance improvement of 59.27% in data retrieval latency and 55.07% in data caching cost.

In the edge computing environment, app vendors can distribute popular data from the cloud to edge servers to provide low-latency data retrieval. A key problem is how to distribute these data from the cloud to edge servers cost-effectively. Under current schemes, a file is divided into some data blocks for parallel transmissions from the cloud to target edge servers. Edge servers can then combine received data blocks to reconstruct the file. While this method expedites the data distribution process, it presents potential drawbacks. It is sensitive to transmission delays and transmission failures caused by runtime exceptions like network fluctuations and server failures. This paper presents EdgeHydra, the first edge data distribution scheme that tackles this challenge through fault tolerance based on erasure coding. Under EdgeHydra, a file is encoded into data blocks and parity blocks for parallel transmission from the cloud to target edge servers. An edge server can reconstruct the file upon the receipt of a sufficient number of these blocks without having to wait for all the blocks in transmission. It also innovatively employs a leaderless block supplement mechanism to ensure the receipt of sufficient blocks for individual target edge servers. These improve the robustness of the data distribution process significantly. Extensive experiments show that EdgeHydra can tolerate delays and failures in individual transmission links effectively, outperforming the state-of-the-art scheme by up to 50.54% in distribution time.

To improve the throughput of Byzantine Fault Tolerance (BFT) consensus protocols, the Directed Acyclic Graph (DAG) topology has been introduced to parallel data processing, leading to the development of DAG-based BFT consensus. However, existing DAG-based works heavily rely on Reliable Broadcast (RBC) protocols for block broadcasting, which introduces significant latency due to the three communication steps involved in each RBC. For instance, DAGRider, a representative DAG-based protocol, exhibits the best latency of 12 steps, considerably higher than non-DAG protocols like PBFT, which only requires 3 steps. To tackle this issue, we propose LightDAG, which replaces RBC with lightweight broadcasting protocols such as Consistent Broadcast (CBC) and Plain Broadcast (PBC). Since CBC and PBC can be implemented in two and one communication steps, respectively, LightDAG achieves low latency.In our proposal, we present two variants of LightDAG, namely LightDAG1 and LightDAG2, each providing a trade-off between the best latency and the expected worst latency. In LightDAG1, every block is broadcast using CBC, which exhibits a best latency of 5 steps and an expected worst latency of 14 steps. Since CBC cannot guarantee the totality property, we design a block retrieval mechanism in LightDAG1 to assist replicas in retrieving missing blocks. LightDAG2 utilizes a combination of PBC and CBC for block broadcasting, resulting in the best latency of 4 steps and an expected worst latency of 12(t+1) steps, where t represents the number of actual Byzantine replicas. Since a Byzantine replica may equivocate through PBC, LightDAG2 prohibits blocks from directly referencing contradictory blocks. To ensure liveness, we propose a mechanism to identify and exclude Byzantine replicas if they engage in equivocation attacks. Extensive experiments have been conducted to evaluate LightDAG, and the results demonstrate its feasibility and efficiency.

Mainstream blockchain systems such as Bitcoin and Ethereum are revolutionizing the financial industry by adopting the Nakamoto consensus protocol, i.e., Proof-of-Work (PoW). Only nodes with sufficient computing resources can work out the PoW difficulties, thereby increasing the mining cost of malicious attackers and ensuring the security of blockchain systems. Such an assumption of having abundant resources leads to drawbacks of low throughput and risk of centralization. In this article, we present Dispatcher, a novel distributed consensus protocol that takes resource heterogeneity into account to ensure resource-aware PoW with high efficiency. Dispatcher introduces a hierarchical topology to offer flexible PoW difficulties tailored for different nodes’ resources. In particular, it utilizes the limited resource of each node to jointly maximize the performance by concurrent mining. Moreover, we design an adaptive incentive mechanism to fit the available resource of blockchain nodes to rewards. Our experiments show that Dispatcher enjoys a substantial performance margin over the state-of-the-art. We can achieve a 50% throughput improvement compared with OHIE.

To parallelize data processing within BFT consensus protocols,Directed Acyclic Graph(DAG) structures have been integrated into consensus design, shaping the realm of DAG-based BFT protocols. Existing DAG-based protocols rely on theReliable Broadcast(RBC) protocol or its variants for block dissemination, which ensures consistency and totality properties of the data delivery. However, the inherent communication overhead ofO(n2) in RBC (wherenis the total replica count) results in an unwieldyO(n3) overhead in current DAG-based solutions, as each replica disseminates blocks through RBC in parallel. In response to this issue, we propose two new broadcast protocols:Provable Broadcast(PBC) andEnhanced ProvableBroadcast (EPBC). Both PBC and EPBC maintain the consistency property of data delivery, similar to RBC, while offering linear communication overhead without totality. Leveraging these broadcast protocols, we devise Wahoo, a novel DAG-based BFT protocol that significantly reduces communication overhead toO(n2). To address the absence of the totality property, we introduce a block retrieval mechanism to assist replicas in acquiring missing blocks. Additionally, under favorable conditions, Wahoo achieves a low latency of 4δ (where δ symbolizes the actual network delay), rivaling the best performance of existing DAG-based protocols. Various experiments showcase Wahoo’s high performance, owing to its substantially reduced communication overhead.

The privacy-preserving query is critical for modern blockchain systems, especially when supporting many crucial applications such as finance and healthcare. Recent advances in blockchain query schemes mainly focus on enhancing the traceability efficiency of integrity authentication. Despite these efforts, we argue that the exposure of retrieval information may result in privacy leakage, which inevitably poses an important yet unresolved challenge. In this paper, we introduce Cloak, a novel privacy-preserving blockchain query scheme with two notable features. First, it utilizes a two-phase distributed query requests technique, i.e., division and aggregation, to hide retrieval information based on the natural independent characteristic of blockchain. Second, we add noise to the sub-request set to avoid malicious attacks during transmission and adopt smart contract-based asymmetric encryption to guarantee the correctness of query results. Experimental results demonstrate that Cloak improves the query performance by up to 4× and reduces the storage overhead by 50% compared with the state-of-the-art Spiral.

Blockchain technology has gained prominence for its potential to address security and privacy challenges in Internet-of-Things (IoT) services and Cyber-Physical Systems (CPS) due to its decentralized, traceable, and immutable nature. However, the considerable energy consumption associated with blockchain, exemplified by Bitcoin, has raised sustainability concerns. This paper introduces BitFT, a consensus protocol that combines the strengths of both lottery-based and voting-based mechanisms to offer a sustainable, comprehensible, and high-performance solution. BitFT dissects the block lifecycle into three phases: dissemination, and commitment phases, which correspond to the Bitcoin framework. It leverages a multiple-round sortition algorithm, a Reliable Broadcast (Rbc) protocol, and a Quorum Certificate (QC) mechanism to facilitate efficient protocol operation. The sortition algorithm functions like a lottery algorithm, while theRbcprotocol andQCmechanism are implemented based on votes. In order to maximize network utilization and enhance system throughput, we further introduce a layered architecture to BitFT, which allows for concurrent commitment of multiple blocks at the same height. We perform a comprehensive analysis to verify the correctness of BitFT and conduct various experiments to demonstrate its high performance.

To reduce latency and communication overhead of asynchronous Byzantine Fault Tolerance (BFT) consensus, an optimistic path is often added, with Ditto and BDT as state-of-the-art representatives. These protocols first attempt to run an optimistic path that is typically adapted from partially-synchronous BFT and promises good performance in good situations. If the optimistic path fails to make progress, these protocols switch to a pessimistic path after a timeout, to guarantee liveness in an asynchronous network. This design crucially relies on an accurate estimation of the network delay Δ to set the timeout parameter correctly. A wrong estimation of Δ can lead to either premature or delayed switching to the pessimistic path, hurting the protocol's efficiency in both cases.

To overcome the scaling and performance limitations, the Directed Acyclic Graph (DAG) is utilized as the underlying storage model of blockchain systems, which enables concurrent transaction processing and confirmation. However, accompanied by high performance, DAG-based blockchains still suffer from the severe challenge of constrained storage scalability, i.e., expensive storage overhead. Based on an in-depth analysis of the data, we discover that the root cause of storage overhead stems from the considerable data redundancy in the DAG-based blockchains. In this paper, we propose GeckoDAG, a lightweight DAG-based blockchain, whose design consists of two steps. First, we abstract a storage model named Basic from the existing DAG-based blockchain systems, which offers both high performance and security. On top of Basic, we then devise GeckoDAG, which merges previous transactions into Transaction Union (TU) and reduces the data redundancy in TU, thus lowering the storage overhead. To evaluate our design, we implement a prototype of GeckoDAG and conduct various experiments on it. The experimental results demonstrate that GeckoDAG can offer storage scalability while maintaining the security and efficiency of DAG-based blockchains.

To enable parallel processing, the Directed Acyclic Graph (DAG) structure is introduced to the design of asyn-chronous Byzantine Fault Tolerant (BFT) consensus protocols, known as DAG-based BFT. Existing DAG-based BFT protocols operate in successive waves, with each wave containing three or four Reliable Broadcast (RBC) rounds to broadcast data, resulting in high latency due to the three communication steps required in each RBC. For instance, Tusk, a state-of-the-art DAG-based BFT protocol, has a good-case latency of 7 communication steps and an expected worst latency of 21 communication steps. To reduce latency, we propose GradedDAG, a new DAG-based BFT consensus protocol based on our adapted RBC called Graded RBC (GRBC) and the Consistent Broadcast (CBC), with each wave consisting of only one GRBC round and one CBC round. Through GRBC, a replica can deliver data with a grade of 1 or 2, and a non-faulty replica delivering the data with grade 2 can ensure that more than 2/3 of replicas have delivered the same data. Meanwhile, through CBC, data delivered by different non-faulty replicas must be identical. In each wave, a block in the GRBC round will be elected as the leader. If a leader block has been delivered with grade 2, it and all its ancestor blocks can be committed. GradedDAG offers a good-case latency of 4 communication steps and an expected worst latency of 7.5 communication steps, significantly lower than the state-of-the-art. Experimental results demonstrate GradedDAG's feasibility and efficiency.

The popularity of blockchain technology has revived interest in Byzantine Fault Tolerance (BFT) consensus protocols. However, existing protocols suffer from high latency, especially when the system is deployed in a worldwide manner. Taking Practical Byzantine Fault Tolerance (PBFT), the best-known and de-facto standard of BFT consensus protocol, as an example, it requires at least three phases to commit a request. Although some works attempt to shorten the number of phases by proposing a fast-path commitment rule, they either sacrifice resilience or subvert security.

A Directed Acyclic Graph (DAG)-based blockchain with its inherent parallel structure can potentially significantly improve the throughput performance over conventional blockchains. Such a performance improvement can be further enhanced through concurrent transaction processing. This, however, brings new challenges in concurrency control design in that there is an increased number of concurrent reads and writes to the same address in a DAG-based blockchain, which leads to a considerable rise of potential conflicts. Therefore, one critical problem is how to effectively and efficiently detect and order conflicting transactions. In this work, for the first time, we aim to improve system throughput and processing latency by exploring the address dependencies among different transactions. We propose NEZHA, an efficient concurrency control scheme for DAG-based blockchains. Specifically, NEZHA intelligently constructs an address-based conflict graph (ACG) while incorporating address dependencies as edges to capture all conflicting transactions. To generate a total order between transactions, we propose a hierarchical sorting (HS) algorithm to derive sorting ranks of addresses based on the ACG and sort transactions on each address. Extensive experiments demonstrate that, even under high data contention, NEZHA can increase the throughput over the conventional conflict graph scheme by up to 8 ×, while decreasing the transaction processing latency up to 10 ×.

It has been recognized that one of the bottlenecks in the UTXO-based blockchain systems is the slow block validation - the process of validating a newly-received block by a node before locally storing it and further broadcasting it. As a block contains multiple inputs, the block validation mainly involves checking the inputs against the status data, which is also known as the Unspent Transaction Outputs (UTXO) set. As time goes by, the UTXO set becomes more and more expansive, most of which can only be stored on disks. This considerably slows down the input checking and thus block validation, which can potentially compromise system security. To deal with the above problem, we disassemble the function of input checking into three parts: existence validation (EV), unspent validation (UV), and script validation (SV). Based on the disassembly, we propose EBV, an efficient block validation mechanism to speed up EV, UV, and SV individually. First, EBV changes the representation of status data, from UTXO set to a bit-vector set, which drastically reduces its size. The smaller status data can be entirely maintained in memory, thereby accelerating UV and also block validation. Second, EBV requires each transaction to carry the proof data, which enables EV and SV without accessing the disks. Furthermore, we also cope with two challenges in the design of EBV, namely transaction inflation and fake positions. To evaluate the EBV mechanism, we implement a prototype on top of Bitcoin, the most widely known UTXO-based blockchain, and conduct extensive experiments to compare EBV and Bitcoin. The experimental results demonstrate that EBV successfully reduces the memory requirement by 93.1 % and the block validation time by up to 93.5%.

With customization of demands and functionalities, diverse blockchain systems are integrated with IoT in different application scenarios, forming a multichain environment. Multichain interoperability has become a crucial emerging issue, i.e., different blockchain systems are difficult to interact with each other credibly and efficiently. It further leads to isolated data islands across multichain. Therefore, it is of significant importance to facilitate data sharing among multichain systems. Toward achieving this, there are two main challenges. First, data across multiple blockchains must be shared reliably to meet the tamper-proof merits of blockchain technology. Second, we must control the data access process in a fine-grained way to protect sensitive data and user privacy. This article proposes SynergyChain, a multichain framework to enable reliable data sharing with controllable data access. By aggregating the data from multiple blockchains and reorganizing it in SynergyChain, we can achieve data reliability with the verification. Meanwhile, SynergyChain provides hierarchical access control based on smart contracts, making access control automated and credible. Experiments show that SynergyChain can support data sharing reliably and efficiently and reduce data query latency compared with multichain data requesting sequentially.

Due to the lack of supervision in the decentralized exchanges (DEXs), arbitrageurs can utilize information and take advantage of price gap to make profits over such platforms such as Ethereum blockchain. DEX arbitrage poses possibilities and opportunities for defrauding and can seriously impair the operation of the Ethereum ecosystem. It motivates this work to explore and characterize the unique features of arbitrage which differ from other frauds such as money laundering and Ponzi games for better detection. This work makes the first attempt for detecting arbitrage on Ethereum through feature fusion and positive-unlabeled learning (PU learning). We first conduct an in-depth analysis and exploit two-fold arbitrage features by fusion including: 1) statistical features that explicitly represent the node activity levels according to expert knowledge; and 2) structural features that implicitly encode the transactions information by graph machine learning. We then apply PU learning to generate negative instances for compensating the imbalanced arbitrage datasets. We evaluate our proposed method through extensive experiments over a real-world dataset and demonstrate that it can achieve 90% accuracy in detecting arbitrage activities on Ethereum.

Recent successful pilot studies on blockchains showed significant benefits of data provenance towards improved visibility and authenticity, as well as a reduction of administrative costs. Traditionally, all the parties reside in a single blockchain system, which leads to various single-chain provenance query schemes. However, for the sake of convenience, it has been a trend for different parties to deploy their own blockchain systems separately, which requires cross-chain provenance queries. Unfortunately, state-of-the-art blockchain query schemes suffered from inauthentic transactions and low efficiencies when expanding to adversarial cross-chain commercial deals. To be more specific, query results may be inconsistent and incomplete over multiple blockchains due to the lack of global knowledge on cross-chain dependencies. Moreover, these schemes perform cross-chain provenance queries in sequence, leading to high query latencies. In this paper, we present Vassago, the first multi-chain system that empowers efficient and authenticated provenance queries. Vassago incorporates three innovative designs: 1) it explores the dependencies of cross-chain transactions, 2) it validates the authenticity of cross-chain provenance by recording and querying the dependencies on a shared blockchain, and 3) it improves efficiency by parallelizing query processes. Our experimental results show that Vassago can shorten the query latency by 85.9% and reduce the storage consumption by up to 85.7%, with negligible overhead.

Federated learning (FL) has been gaining popularity as a way to provide privacy-preserving data sharing for the Internet of Medical Things (IoMT). As a complementary, blockchain technology is used in recent literature to make FL secure. However, existing blockchain-based FL (BFL) solutions do not perform well when data in a BFL cluster are sparse. A direct solution is to collect as many devices as possible to establish a large BFL cluster. However, these devices may locate in geographically distant areas and be separated by great distance, which further results in high communication latency. The high latency will lead to BFL’s low system efficiency due to frequent communications in the blockchain consensus. In this article, we propose that the large cluster should be divided into multiple smaller clusters, each in its own geographical area and organized with a BFL. In this context, we propose CFL, a cross-cluster FL system facilitated by the cross-chain technique. CFL connects multiple BFL clusters, where only a few aggregated updates are transmitted over long distances across clusters, thus improving the system efficiency. The design of CFL focuses on a cross-chain consensus protocol, which guarantees the model updates to be exchanged securely across clusters. We carry out extensive experiments to evaluate CFL in comparison with BFL, and show both CFL’s feasibility and efficiency.

In the Bitcoin system, transaction history of an address can be useful in many scenarios, such as the balance calculation and behavior analysis. However, it is non-trivial for a common user who runs a light node to fetch historical transactions, since it only stores headers without any transaction details. It usually has to request a full node who stores the complete data. Validation of these query results is critical and mainly involves two aspects: correctness and completeness. The former can be implemented via Merkle branch easily, while the latter is quite difficult in the Bitcoin protocol. To enable the completeness validation, a strawman design is proposed, which simply includes the BF (Bloom filter) in the headers. However, since the size of BF is about KB, light nodes in the strawman will suffer from the incremental storage burden. What's worse, an integrated block must be transmitted when BF cannot work, resulting in large network overhead. In this paper, we propose LVQ, the first lightweight verifiable query approach that reduces the storage requirement and network overhead at the same time. To be specific, by only storing the hash of BF in headers, LVQ keeps data stored by light nodes being little. Besides, LVQ introduces a novel BMT (BF integrated Merkle Tree) structure for lightweight query, which can eliminate the communication costs of query results by merging the multiple successive BFs. Furthermore, when BF cannot work, a lightweight proof by SMT (Sorted Merkle Tree) is exploited to further reduce the network overhead. The security analysis confirms LVQ's ability to enable both correctness and completeness validation. In addition, the experimental results demonstrate its lightweight.

IoT-blockchain applications have advantages of managing massive IoT devices, achieving advanced data security, and data credibility. However, there are still some challenges when deploying IoT applications on blockchain systems due to limited storage, power, and computing capability of IoT devices. Applying current consensus protocols to IoT applications may be vulnerable to Sybil node attacks or suffer from high-computational cost and low scalability. In this paper, we propose G-PBFT (Geographic-PBFT), a new location-based and scalable consensus protocol designed for IoT-blockchain applications. The principle of G-PBFT is based on the fact that most IoT-blockchain applications rely on fixed IoT devices for data collection and processing. Fixed IoT devices have more computational power than other mobile IoT devices, e.g., mobile phones and sensors, and are less likely to become malicious nodes. G-PBFT exploits geographic information of fixed IoT devices to reach consensus, thus avoiding Sybil attacks. In G-PBFT, we select those fixed, loyal, and capable nodes as endorsers, reducing the overhead for validating and recording transactions. As a result, G-PBFT achieves high consensus efficiency and low traffic intensity. Moreover, G-PBFT uses a new era switch mechanism to handle the dynamics of the IoT network. To evaluate our protocol, we conduct extensive experiments to compare the performance of G-PBFT against existing consensus protocol with over 200 participating nodes in a blockchain system. Experimental results demonstrate that G-PBFT significantly reduces consensus time, network overhead, and is scalable for IoT applications.

Blockchain technology offers an intelligent amalgamation of distributed ledger, Peer-to-Peer (P2P), cryptography, and smart contracts to enable trustworthy applications without any third parties. Existing blockchain systems have successfully either resolved the scalability issue by advancing the distributed consensus protocols from the control plane, or complemented the security issue by updating the block structure and encryption algorithms from the data plane. Yet, we argue that the underlying P2P network plane remains as an important but unaddressed barrier for accelerating the overall blockchain system performance, which can be discussed from how fast and reliable the network is. In order to improve the blockchain network performance about enabling fast and reliable broadcast, we establish a trust-enhanced blockchain P2P topology which takes transmission rate and transmission reliability into consideration. Transmission rate reflects blockchain network speed to disseminate transactions and blocks, and transmission reliability reveals whether transmission rate changes drastically on unreliable network connection. This paper presents BlockP2P-EP, a novel trust-enhanced blockchain topology to accelerate transmission rate and meanwhile retain transmission reliability. BlockP2P-EP first operates the geographical proximity sensing clustering, which leverages K-Means algorithm for gathering proximity peer nodes into clusters. It follows by the hierarchical topological structure that ensures strong connectivity and small diameter based on node attribute classification. Then we propose establishing trust-enhanced network topology. On top of the trust-enhanced blockchain topology, BlockP2P-EP conducts the parallel spanning tree broadcast algorithm to enable fast data broadcast among nodes both intra- and inter- clusters. Finally, we adopt an effective node inactivation detection method to reduce network load. To verify the validity of BlockP2P-EP protocol, we carefully design and implement a blockchain network simulator. Evaluation results show that BlockP2P-EP can exhibit promising network performance in terms of transmission rate and transmission reliability compared to Bitcoin and Ethereum.

The pioneer blockchain platform Bitcoin has drawn massive attention from various sectors in society, with its promising decentralized, irreversible, and trust-free natures. Increasing volume of Bitcoin transactions makes it impractical for each user to store a full copy of whole ledger, especially for a normal user who makes few transactions and owns constrained storage space. There are already some works conducted to reduce the storage overhead by redesigning the system protocol, based on different trust assumptions. An example is running light nodes, which trust the full nodes to store its relevant data and reply to its data request timely. However, it introduces the risks of permanent data loss, as the data relevant to a light node may be tailored by all the full nodes later. Another attempt is conducted by organizing the nodes into several cooperative units based on a trust assumption, which is hard to satisfy in practice. In this paper, we propose a novel Jigsaw-like Data Reduction (Jidar) approach. Each node in Jidar only stores transactions of interest and relevant Merkle branches from the complete blocks, just like selecting several pieces from the jigsaw puzzles. A node can maintain and verify all its relevant data locally and safely without any trust assumptions. To verify the validity of a newly proposed transaction, Merkle branches relevant to the inputs are provided along with the transaction by the proposer. In view of the requirement that a user wants to cohere all the fragments stored in different nodes into a complete block, just like stitching all the pieces into a complete jigsaw-puzzle picture, a mechanism to query full data is added to Jidar. Experimental results demonstrate that Jidar can reduce a normal node's storage cost to about 1.03% of the original Bitcoin system at a low communication cost

Modern bookcrossing leverages the mobile networks to help readers share books via convenient connection, and thus expedites the dissemination of information. However, the lack of traceability has significantly hindered the wide adoption of mobile bookcrossing, leading to loss of books. Meanwhile, the managerial inefficiency of current mobile bookcrossing systems becomes another obstacle for advancing the book circulation. To overcome these problems, we propose to leverage the promising blockchain technology with the merits of its immutability and cryptographic smart contract. In this paper, we present BookChain, a traceable and efficient blockchain-based innercampus book sharing system. BookChain stores the complete sharing data of an interested book permanently on blockchain, such that every reader can trace the borrowing history, which reduces the potential for loss of book. BookChain also introduces the use of smart contract to automate the circulation of books with minimal human intervention, resulting in the improvement of efficiency. The experimental results show the effectiveness and low-cost of the proposed system under high concurrent users.

Next-generation blockchain ecosystem will be fuelled by a large variety of blockchain systems. These systems increasingly demand proper cross-chain cooperation to provide richer functionalities and enhanced capabilities in the future landscape. How to enable such `interoperability' - effective communication and efficient data transfer across multiple blockchain systems is thus critical and facing many unprecedented theoretical and practical challenges. In this paper, we first clarify the definition of interoperability based on the cross disciplinary nature. Second, a roadmap of challenges needed to be addressed for interoperability has been laid out. Third, we articulate novel architectural approaches to fill in the gap by enforcing the interoperability from different blockchain layers.

With the explosive growth of smartphones and cloud computing, mobile cloud, which leverages cloud resource to boost the performance of mobile applications, becomes attrac- tive. Many efforts have been made to improve the performance and reduce energy consumption of mobile devices by offloading computational codes to the cloud. However, the offloading cost caused by the cloud platform has been ignored for many years. In this paper, we propose Rattrap, a lightweight cloud platform which improves the offloading performance from cloud side. To achieve such goals, we analyze the characteristics of typical of- floading workloads and design our platform solution accordingly. Rattrap develops a new runtime environment, Cloud Android Container, for mobile computation offloading, replacing heavy- weight virtual machines (VMs). Our design exploits the idea of running operating systems with differential kernel features inside containers with driver extensions, which partially breaks the limitation of OS-level virtualization. With proposed resource sharing and code cache mechanism, Rattrap fundamentally improves the offloading performance. Our evaluation shows that Rattrap not only reduces the startup time of runtime environments and shows an average speedup of 16x, but also saves a large amount of system resources such as 75% memory footprint and at least 79% disk capacity. Moreover, Rattrap improves offloading response by as high as 63% over the cloud platform based on VM, and thus saving the battery life.