Blockchain systems based on a reusable resource, such as proof-of-stake (PoS), provide weaker security guarantees than those based on proof-of-work. Specifically, they are vulnerable to long-range attacks, where an adversary can corrupt prior participants in order to rewrite the full history of the chain. To prevent this attack on a PoS chain, we propose a protocol that checkpoints the state of the PoS chain to a proof-of-work blockchain such as Bitcoin. Our checkpointing protocol hence does not rely on any central authority. Our work uses Schnorr signatures and leverages Bitcoin recent Taproot upgrade, allowing us to create a checkpointing transaction of constant size. We argue for the security of our protocol and present an open-source implementation that was tested on the Bitcoin testnet.
With the increase of data stored on the blockchain, the efficiency of storage and calculation of blockchain has gradually become a bottleneck restricting the development of blockchain. By storing data on multiple chains, blockchains can request data from other chains for calculation and the storage pressure can be alleviated. But the transfer of a large amount of data between chains suffers from low transfer efficiency and poor security. A reasonable design is to perform the calculation on the data storage chain and only transfer the results across chains. However, since the calculation process is invisible, blockchains cannot judge the consistency of calculation results from other chains. In this paper, we provide a blockchain-enabled decentralized consistency verification scheme for cross-chain calculation (BeDCV). Considering the decentralized characteristic of blockchain, we adopt the blockchain calledsupervision chainfor decentralized auditing. We modify paillier homomorphic encryption to encrypt data involved in the calculation for correctness verification. Then, we aggregate the ciphertexts of data to generate the audit proof for integrity verification. Besides, we verify whether the data involved in the calculation are real-time by leveraging a counting bloom filter. The supervision chain can check the correctness, integrity, and real-time performance of cross-chain data calculation without revealing any original information about the data. The theoretical and experimental analysis demonstrates that BeDCV can verify the consistency of cross-chain data calculation result effectively, realizing secure and reliable expansion of blockchain.
James E. Short, Ken Miyachi, Christian D. Toouli, Steve Todd
The rapid upswing in interest in federated learning (FL) and federated analytics (FA) architectures has corresponded with the rapid increase in commercial AI software products, ranging from face detection and language translation to connected IOT devices, smartphones, and autonomous vehicles equipped with high-resolution sensors. However, the traditional client-server model does not readily address questions of data ownership, privacy, and data location in the context of the multiple datasets required for machine learning. In this paper, we report on a pilot distributed ledger and smart contract network model, designed to track analytic jobs in an HPC supercomputing environment. The test system design integrates the FL/FA model into a blockchain-based network architecture, wherein the test system records interactions with the global server and blockchain network. The design goal is to create a secure audit trail of supercomputer analytic operations and the ability to securely federate those operations across multiple supercomputer deployments. As there are still relatively few real-world applications of FL/FA models and blockchain networks in use, our system design, test deployment, and sample code are intended to provide interested researchers with exploratory tools for future research.
The evolution of smart contracts in recent years inspired a crucial question: do smart contract evaluation protocols provide the required level of privacy when executing contracts on the blockchain? The Hawk (IEEE S&P ’16) paper introduces a way to solve the problem of privacy in smart contracts by evaluating the contracts off-chain, albeit with the trust assumption of a manager. To avoid the partially trusted manager altogether, a novel approach named zkHawk (IEEE BRAINS ’21) explains how we can evaluate the contracts privately off-chain using a multi-party computation (MPC) protocol instead of trusting said manager. This paper dives deeper into the detailed construction of a variant of the zkHawk protocol titled V-zkHawk using formal proofs to construct the said protocol and model its security in the universal composability (UC) framework (FOCS ’01). The V-zkHawk protocol discussed here does not support immediate closure, i.e., all the parties (n) have to send a message to inform the blockchain that the contract has been executed with corruption allowed for up to t parties, where t<n. In the most quintessential sense, the V-zkHawk is a variant because the outcome of the protocol is similar (i.e., execution of smart contract via an MPC function evaluation) to zkHawk, but we modify key aspects of the protocol, essentially creating a small trade-off (removing immediate closure) to provide UC (stronger) security. The V-zkHawk protocol leverages joint Schnorr signature schemes, encryption schemes, Non-Interactive Zero-Knowledge Proofs (NIZKs), and commitment schemes with Common Reference String (CRS) assumptions, MPC function evaluations, and assumes the existence of asynchronous, authenticated broadcast channels. We achieve malicious security in a dishonest majority setting in the UC framework.
This paper proposes a protocol for Proof of As-sets of a bitcoin exchange using the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (ZK-SNARK) without revealing either the bitcoin addresses of the exchange or balances associated with those addresses. The proof of assets is a mech-anism to prove the total value of bitcoins the exchange has the authority to spend using its private keys. We construct a privacy-preserving ZK-SNARK proof system to prove the knowledge of the private keys corresponding to the bitcoin assets of an exchange. The ZK-SNARK toolchain helps to convert an NP-Statement for proving the knowledge of the private keys (known to the exchange) into a circuit satisfiability problem. In this protocol, the exchange creates a Pedersen commitment to the value of bitcoins associated with each address without revealing the balance. The simulation results show that the proof generation time, size, and verification time are efficient in practice.
The fair exchange problem has faced for a long time the bottleneck of a required trusted third party. The recent development of blockchains introduces a new type of party to this problem, whose trustworthiness relies on a public ledger and distributed computation. The challenge in this setting is to reconcile the minimalistic and public nature of blockchains with elaborate fair exchange requirements, from functionality to privacy. Zero-knowledge contingent payments (ZKCP) are a class of protocols that are promising in this direction, allowing the fair exchange of data for payment. We propose a new ZKCP protocol that, when compared to others, requires less computation from the blockchain and less interaction between parties. The protocol is based on two-party (weak) adaptor signatures, which we show how to instantiate from state of the art multiparty signing protocols. We improve the symbolic definition of ZKCP security and, for automated verification with Tamarin, we propose a general security reduction from the theory of abelian groups to the theory of exclusive or.
Blockchains are meant to be persistent: posted transactions are immutable and cannot be changed. When a theft takes place, there are limited options for reversing the disputed transaction, and this has led to significant losses in the blockchain ecosystem. In this paper we propose reversible versions of ERC-20 and ERC-721, the most widely used token standards. With these new standards, a transaction is eligible for reversal for a short period of time after it has been posted on chain. After the dispute period has elapsed, the transaction can no longer be reversed. Within the short dispute period, a sender can request to reverse a transaction by convincing a decentralized set of judges to first freeze the disputed assets, and then later convincing them to reverse the transaction. Supporting reversibility in the context of ERC-20 and ERC-721 raises many interesting technical challenges. This paper explores these challenges and proposes a design for our ERC-20R and ERC-721R standards, the reversible versions of ERC-20 and ERC-721. We also provide a prototype implementation. Our goal is to initiate a deeper conversation about reversibility in the hope of reducing some of the losses in the blockchain ecosystem.
Abstract Widely adopted blockchain-based fair data exchange protocol have the following problems in multi-party scenario: 1) in multi-buyer v.s. one seller scenario, the negotiation for data availability verification between the buyer and seller reduces transaction throughput greatly. Besides, malicious buyers can launch DoS attacks to prevent the seller from serving others by requiring lots of data availability proofs. 2) in multi-seller v.s. one buyer scenario where the buyer pays for the merged data of all sellers, current protocols treat this as multiple independent transactions, resulting in excessive on-chain costs. Moreover, current protocols neglect that data ownership establishment on-chain may be tampered since the registration info is in plaintext and submitted through the Internet. This paper proposes multi-party non-interactive atomic fair data exchange protocol based on blockchain to solve the above problems, providing data confidentiality, transaction atomic fairness, data intellectual property right protection, and high efficiency. Specifically, we propose transparent zero knowledge proof-based data verification guaranteeing the data confidentiality and transaction fairness. With the proof computed once and used everywhere, transaction throughput is improved greatly and DoS attacks initiated by malicious buyers is prevented. Moreover, the agent representing multi-seller is introduced to reduce on-chain costs. Furthermore, two-stage on-chain ownership registration is proposed to prevent eavesdroppers from impersonating the owner. Finally, we implement a POC (Proof of Concept) of our protocol as the BADE (Blockchain-based multi-party non-interactive Atomic fair Data Exchange). Experiments show that, our throughput within 12 hours is 50 times that of existing solution\cite{ref10}. And in multi-seller scenario, the on-chain gas costs of our protocol is reduced by 19.9\%-30.5\% in different seller/buyer ratios. The fairness of data exchange is also ensured by extra gas costs of dishonest party.
Hanaa Abbas, Maurantonio Caprolu, Roberto Di Pietro
Polkadot is a network protocol launched in 2020 with the ambition of unlocking the full potential of blockchain technologies. Its novel multi-chain protocol allows arbitrary data to be transferred across heterogeneous blockchains, enabling the implementation of a wide range of novel use cases. The Polkadot architecture is based on the principles of sharding, which promises to solve scalability and interoperability shortcomings that encumber many existing blockchain-based systems. Lured by these impressive features, investors immediately appreciated the Polkadot project, which is now firmly ranked among the top 10 cryptocurrencies by capitalization (around 20 Billions USD). However, Polkadot has not received the same level of attention from academia that other proposals in the crypto domain have received so far, like Bitcoin, Ethereum, and Algorand, to cite a few. Polkadot architecture is described and discussed only in the grey literature, and very little is known about its internals. In this paper, we provide the first systematic study on the Polkadot environment, detailing its protocols, governance, and economic model. Then, we identify several limitations -- supported by an empirical analysis of its ledger -- that could severely affect the scalability and overall security of the network. Finally, based on our analysis, we provide future directions to inspire researchers to investigate further the Polkadot ecosystem and its pitfalls in terms of performance, security, and network aspects.
Lorenzo Ceragioli, Letterio Galletta, Pierpaolo Degano, Luca Viganò
In a decentralized environment, exchanging resources requires users to bargain until an agreement is found. Moreover, human agreements involve a combination of collaborative and selfish behavior and often induce circularity, complicating the evaluation of exchange requests. We introduce MuAC, a policy language that allows users to state in isolation under which conditions they are open to grant their resources and what they require in return. In MuAC, exchange requests are evaluated automatically with the guarantee that the only exchanges that will take place are those that mutually satisfy users' conditions. Moreover, MuAC can be used as an enforcement mechanism to prevent users from cheating. As a proof of concept, we implement a blockchain smart contract that allows users to exchange their non-fungible tokens.
While users enjoy the convenience of data outsourcing in the cloud, they also face the risks of data modification and private information leakage. Searchable encryption technology can perform keyword searches over encrypted data while protecting their privacy and guaranteeing the integrity of the data by verifying the search results. However, some associated problems are still encountered, such as the low efficiency of verification and uncontrollable query results. Accordingly, this paper proposes a Privacy-Preserving Searchable Encryption (PPSE) scheme based on public and private blockchains. First, we store an encrypted index in a private blockchain while outsourcing corresponding encrypted documents to a public blockchain. The encrypted documents are located through the encrypted index. This method can reduce the storage overhead on the blockchains, and improve the efficiency of transaction execution and the security of stored data. Moreover, we adopt a smart contract to introduce a secondary verification access control mechanism and restrict data users' access to the private blockchain through authorization for the purpose of guaranteeing data privacy and the correctness of access control verification. Finally, the security analysis and experimental results indicate that compared with existing schemes, the proposed scheme can not only improve the security of encrypted data but also guarantee the efficiency of the query.
Servio Palacios, Aaron Ault, James V. Krogmeier, Bharat Bhargava · 5 authors
This paper introduces AGAPECert, an Auditable, Generalized, Automated, Privacy-Enabling, Certification framework capable of performing auditable computation on private data and reporting real-time aggregate certification status without disclosing underlying private data. AGAPECert utilizes a novel mix of trusted execution environments, blockchain technologies, and a real-time graph-based API standard to provide automated, oblivious, and auditable certification. Our technique allows a privacy-conscious data owner to run pre-approved Oblivious Smart Contract code in their own environment on their own private data to produce Private Automated Certifications. These certifications are verifiable, purely functional transformations of the available data, enabling a third party to trust that the private data must have the necessary properties to produce the resulting certification. Recently, a multitude of solutions for certification and traceability in supply chains have been proposed. These often suffer from significant privacy issues because they tend to take a" shared, replicated database" approach: every node in the network has access to a copy of all relevant data and contract code to guarantee the integrity and reach consensus, even in the presence of malicious nodes. In these contexts of certifications that require global coordination, AGAPECert can include a blockchain to guarantee ordering of events, while keeping a core privacy model where private data is not shared outside of the data owner's own platform. AGAPECert contributes an open-source certification framework that can be adopted in any regulated environment to keep sensitive data private while enabling a trusted automated workflow.
Nahida Islam, Md. Sazzadur Rahman, Imtiaz Mahmud, Md. Nur Amin Sifat · 5 authors
The world is facing an urgent need to provide secure communication and data access control in advanced metering infrastructure (AMI) because conventional cryptographic key management and authentication protocols are at stake. The cryptography schemes entirely rely on trusted third parties (TTPs), leading to a single point of failure and increasing network overhead. In response to this inefficiency and security compromise, this study proposes a blockchain-enabled distributed AMI secure communication scheme. In the proposed work, smart contract (SC), an integrated part of the blockchain, is programmed to substitute traditional TTP-based transaction systems, which operate in a distributed, immutable, and trustworthy manner. In this paper, we implemented practical Byzantine fault tolerance (PBFT) consensus algorithm and Hyperledger Fabric (HLF) blockchain platform to ensure Byzantine fault tolerance in the blockchain transaction. Performance analysis shows that the proposed BC-AMI scheme has the advantage of incurring the least amount of communication and time costs compared with similar studies while ensuring security against some common cyber-attacks.
In recent years, blockchain holds promise to impact a wide range of application areas, but it still suffers from technical challenges such as security and scalability. The increase in the number of transactions puts blockchains under data storage pressure. The emergence of cross-chain technologies connects different blockchains and relieves the data storage pressure. However, the existing research generally focuses on the technical realization of cross chain, lacking in-depth research on consistency issues like data integrity verification of cross-chain interaction. In this paper, we propose a decentralized cross-chain data integrity verification scheme (DCIV) from the point of view of governing the chain by chain. We adopt supervision chain to audit the integrity of data in cross-chain interaction. We preprocess the off-chain original data in the form of Merkle tree. Before cross-chain interaction, we process the data with KZG polynomial commitment. During the auditing period, the supervision chain generates a challenge and verifies the integrity of cross-chain data. In particular, we add audit digests into the structure of transactions in blockchain to reduce the storage burden during auditing. Theoretical and experimental analyses demonstrate that DCIV can verify the integrity of data in cross-chain interaction, achieving secure and accurate cross-chain data sharing.
Security and privacy issues have become a major hindrance to the broad adoption of Ride-Hailing Services (RHSs). In this article, we introduce a new collusion attack initiated by the Ride-Hailing Service Provider (RHSP) and a driver that could easily link the real riders and their anonymous requests (credentials). Besides this attack, existing work requires heavy computations to execute user matching, and it is challenging for riders to verify matching results. Meanwhile, a malicious driver may cancel an assigned ride order due to its short distance. To address these issues, we present a RHS system named Nereus to support collusion resistance, efficiency, verifiability, and accountability. First, we integrate a smart contract into a Software Guard Extensions (SGX) enclave to establish aprivate smart contractfor collusion resistance. We use a Bloom filter to achieve efficient matching. Second, we leverage privacy-preserving range query and Merkle proofs to make matching results verifiable. Meanwhile, we adopt short group signatures to provide anonymous authentication and deposit commitments to hold the runaway driver accountable. We formally state and prove the security and privacy of Nereus. We build a prototype based on Ethereum and SGX to conduct extensive performance analysis in regard to gas costs, computational costs, and communication overhead. Experimental results show that Nereus significantly improves over existing schemes in terms of computational costs.
Di Wu, Nai Wang, Jiale Zhang, Yuan Zhang · 6 authors
With the expansion of the Internet of Things (IoT) development and application, federated learning has gained higher popularity in industrial researching fields. However, the security issues in federated learning have become hot-spots in the research area, such as privacy-preserving and poisoning attacks. This paper proposes a robust blockchained multi-layer decentralized federated learning (RBML-DFL) framework to ensure the federated learning's robustness. Firstly, by adopting the three-layered framework, the blockchain connects the federated learning components to secure the privacy and data safety of federated learning. Secondly, the proposed framework provides resilience on poisoning attacks to the central model compared to typical federated learning frameworks. Lastly, the decentralized structure associated with the blockchain tracing back mechanism can prevent the central server failure or mal-function compared to centralized federated learning. We evaluate and compare the proposed framework with other state-of-the-art federated learning frameworks on the accuracy, latency, and system robustness under poisoning attacks. The results show that the proposed RBML-DFL framework outperforms state-of-the-art baseline frameworks on all three metrics: accuracy, latency, and the robustness of the federated learning.
Ertem Nusret Tas, David Tse, Fangyu Gai, Sreeram Kannan · 6 authors
Bitcoin is the most secure blockchain in the world, supported by the immense hash power of its Proof-of-Work miners. Proof-of-Stake chains are energy-efficient, have fast finality but face several security issues: susceptibility to non-slashable long-range safety attacks, low liveness resilience and difficulty to bootstrap from low token valuation. We show that these security issues are inherent in any PoS chain without an external trusted source, and propose a new protocol, Babylon, where an off-the-shelf PoS protocol checkpoints onto Bitcoin to resolve these issues. An impossibility result justifies the optimality of Babylon. A use case of Babylon is to reduce the stake withdrawal delay: our experimental results show that this delay can be reduced from weeks in existing PoS chains to less than 5 hours using Babylon, at a transaction cost of less than 10K USD per annum for posting the checkpoints onto Bitcoin.
Marco Benedetti, Francesco De Sclavis, Marco Favorito, Giuseppe Galano · 7 authors
Distributed Ledger Technologies (DLTs), when managed by a few trusted validators, require most but not all of the machinery available in public DLTs. In this work, we explore one possible way to profit from this state of affairs. We devise a combination of a modified Practical Byzantine Fault Tolerant (PBFT) protocol and a revised Flexible Round-Optimized Schnorr Threshold Signatures (FROST) scheme, and then we inject the resulting proof-of-authority consensus algorithm into Bitcoin (chosen for the reliability, openness, and liveliness it brings in), replacing its PoW machinery. The combined protocol may operate as a modern, safe foundation for digital payment systems and Central Bank Digital Currencies (CBDC).
Probabilistically Checkable Proofs (PCPs) allows a randomized verifier, with oracle access to a purported proof, to probabilistically verify an input statement of the form “x∈L” by querying only a few proof bits. Zero-Knowledge PCPs (ZK-PCPs) enhance standard PCPs to additionally guarantee that the view of any (possibly malicious) verifier querying a bounded number of proof bits can be efficiently simulated up to a small statistical distance. The first ZK-PCP construction of Kilian, Petrank and Tardos (STOC 1997), and following constructions employing similar techniques, necessitate that the honest verifier makes several rounds of queries to the proof. This undesirable property, which is inherent to their technique, translates into increased round complexity in cryptographic applications of ZK-PCPs. We survey two recent ZK-PCP constructions—due to Ishai, Yang and Weiss (TCC 2016-A), and Hazay, Venkitasubramaniam and Weiss (ITC 2021)—in which the honest verifier makes a single round of queries to the proof. Both constructions use entirely different techniques compared to previous ZK-PCP constructions, by showing connections to the seemingly-unrelated notion of leakage resilience. These constructions are incomparable to previous ZK-PCP constructions: while on the one hand the honest verifier only makes a single round of queries to the proof, these ZK-PCPs either obtain a smaller (polynomial) ratio between the query complexity of the honest and malicious verifiers or obtain a weaker ZK guarantee in which the ZK simulator is not necessarily efficient.
YASH RAJPUT, Chaitanya Kale -, Santosh Kumar -, Asam Bhanu Prakash - · 5 authors
Know Your Customer (KYC) verification is an essential regulatory procedure in financial services to prevent fraud, money laundering and other financial crimes. Conventional approaches are centralized, redundant across institutions, and prone to data breaches. This paper presents a decentralized framework that combines blockchain smart contracts and InterPlanetary File System (IPFS) for immutably recording KYC document fingerprints while storing actual documents off-chain. We describe the system architecture, implementation choices, security and privacy considerations, and evaluation metrics. The paper includes a comparison between traditional and blockchainenabled KYC systems and discusses future directions such as Decentralized Identifiers (DIDs) and Zero-Knowledge Proofs(ZKPs).
Victor Youdom Kemmoe, Yongseok Kwon, Rasheed Hussain, Sunghyun Cho · 5 authors
Group Key Exchange (GKE) is an important tool to develop secure multi-user applications such as group text messages, ad-hoc networks, and so on. Most of the currently deployed GKE schemes are synchronous, i.e., they require all the participants to be online during their execution. However, with more battery-powered devices being used in such applications, the synchronicity requirement is challenging to fulfill. To fill the gaps, asynchronous GKE schemes have been introduced in the literature. Nevertheless, the currently available asynchronous and synchronous GKE schemes rely on Trusted Third Parties (TTPs) for key establishment and management. To this end, reliance on TTPs is a serious shortcoming since TTPs are well known to be the single point of failure. Furthermore, the existing GKE schemes require participants to perform all computations, which can degrade the performance of resource-constrained devices such as Internet of Things (IoT) devices. To solve these problems, in this paper, we propose an asynchronous GKE scheme that uses blockchain and smart contracts to store the security keys-related material and reduce the computational load of the participants. Furthermore, our proposed scheme provides Perfect Forward Secrecy (PFS) and Post-Compromised Security (PCS). Our implementation on Ethereum shows that the proposed scheme can scale to more than 100 participants when combined with a distributed storage system.
Matheus V. X. Ferreira, Ye Lin Sally Hahn, S. Matthew Weinberg, Catherine Yu
Cryptographic Self-Selection is a subroutine used to select a leader for modern proof-of-stake consensus protocols, such as Algorand. In cryptographic self-selection, each round $r$ has a seed $Q_r$. In round $r$, each account owner is asked to digitally sign $Q_r$, hash their digital signature to produce a credential, and then broadcast this credential to the entire network. A publicly-known function scores each credential in a manner so that the distribution of the lowest scoring credential is identical to the distribution of stake owned by each account. The user who broadcasts the lowest-scoring credential is the leader for round $r$, and their credential becomes the seed $Q_{r+1}$. Such protocols leave open the possibility of a selfish-mining style attack: a user who owns multiple accounts that each produce low-scoring credentials in round $r$ can selectively choose which ones to broadcast in order to influence the seed for round $r+1$. Indeed, the user can pre-compute their credentials for round $r+1$ for each potential seed, and broadcast only the credential (among those with a low enough score to be the leader) that produces the most favorable seed. We consider an adversary who wishes to maximize the expected fraction of rounds in which an account they own is the leader. We show such an adversary always benefits from deviating from the intended protocol, regardless of the fraction of the stake controlled. We characterize the optimal strategy; first by proving the existence of optimal positive recurrent strategies whenever the adversary owns last than $38\%$ of the stake. Then, we provide a Markov Decision Process formulation to compute the optimal strategy.
Federated learning enables multiple users to collaboratively train a global model using the users’ private data on users’ local machines. This way, users are not required to share their training data with other parties, maintaining user privacy; however, the vanilla federated learning proposal is mainly assumed to be run in a trusted environment, while the actual implementation of federated learning is expected to be performed in untrusted domains. This paper aims to use blockchain as a trusted federated learning platform to realize the missing “running on untrusted domain” requirement. First, we investigate vanilla federate learning issues such as client’s low motivation, client dropouts, model poisoning, model stealing, and unauthorized access. From those issues, we design building block solutions such as incentive mechanism, reputation system, peer-reviewed model, commitment hash, and model encryption. We then construct the full-fledged blockchain-based federated learning protocol, including client registration, training, aggregation, and reward distribution. Our evaluations show that the proposed solutions made federated learning more reliable. Moreover, the proposed system can motivate participants to be honest and perform best-effort training to obtain higher rewards while punishing malicious behaviors. Hence, running federated learning in an untrusted environment becomes possible.