Abstract Offshore wind farms will play a vital role in the global ambition of net zero energy generation. Future offshore wind farms will be larger and further from the coast, meaning that traditional humanâbased operations and maintenance approaches will become infeasible due to safety, cost, and skills shortages. The use of remotely operated or autonomous robotic assistants to undertake these activities provides an attractive alternative solution. This paper presents an autonomous multirobot system which is able to transport, deploy and retrieve a wind turbine blade inspection robot using an unmanned aerial vehicle (UAV). The proposed solution is a fully autonomous system including a robot deployment interface for deployment, a mechatronic linkâhook module (LHM) for retrieval, both installed on the underside of a UAV, a mechatronic onâload attaching module installed on the robotic payload and an intelligent global mission planner. The LHM is integrated with a 2âDOF hinge that can operate either passively or actively to reduce the swing motion of a slung load by approximately 30%. The mechatronic modules can be coupled and decoupled by special maneuvers of the UAV, and the intelligent global mission planner coordinates the operations of the UAV and the mechatronic modules for synchronous and seamless actions. For navigation in the vicinity of wind turbine blades, a visualâbased localization merged with the location knowledge from Global Navigation Satellite System has been developed. A proofâofâconcept system was field tested on a fullâsize decommissioned windâturbine blade. The results show that the experimental system is able to deploy and retrieve a robotic payload onto and from a wind turbine blade safely and robustly without the need for human intervention. The vicinity localization and navigation system have shown an accuracy of 0.65 and 0.44 m in the horizontal and vertical directions, respectively. Furthermore, this study shows the feasibility of systems toward autonomous inspection and maintenance of offshore windfarms.
Delphinus cross-chain aggregator is a universal firmware which synchronise states between different smart contracts on different block-chains. In the world of block-chains, synchronization challenges are two-folded. Firstly, contracts from different main block-chain can not communicate with each other which makes it hard to establish a trustworthy communication channel for them to share and maintain a universal state between each other. Secondly, transactions on different block-chains can hardly be ordered thus conflicts are common and we need a novel way to avoid and handle these conflicts. Delphinus cross-chain aggregator is a ZKSNARK based multi-block-chain layer on top of which rich cross chain applications can run safely and efficiently.
Matthias Babel, Vincent Gramlich, Marc-Fabian KÜrner, Johannes Sedlmeir ¡ 6 authors
Abstract In the energy transition, there is an urgent need for decreasing overall carbon emissions. Against this background, the purposeful and verifiable tracing of emissions in the energy system is a crucial key element for promoting the deep decarbonization towards a net zero emission economy with a market-based approach. Such an effective tracing system requires end-to-end information flows that link carbon sources and sinks while keeping end consumersâ and businessesâ sensitive data confidential. In this paper, we illustrate how non-fungible tokens with fractional ownership can help to enable such a system, and how zero-knowledge proofs can address the related privacy issues associated with the fine-granular recording of stakeholdersâ emission data. Thus, we contribute to designing a carbon emission tracing system that satisfies verifiability, distinguishability, fractional ownership, and privacy requirements. We implement a proof-of-concept for our approach and discuss its advantages compared to alternative centralized or decentralized architectures that have been proposed in the past. Based on a technical, data privacy, and economic analysis, we conclude that our approach is a more suitable technical backbone for end-to-end digital carbon emission tracing than previously suggested solutions.
Hongjian Yin, E Chen, Yan Zhu, Rongquan Feng ¡ 5 authors
In this paper, we address the problem of secure decision of membership. We present a Zero-Knowledge Dual Membership Proof (ZKDMP) protocol, which can support positive and negative (Pos-and-Neg) membership decisions simultaneously. To do it, two secure aggregation functions are used to compact an arbitrarily-sized subset into an element in a cryptographic space. By using these aggregation functions, a subset can achieve a secure representation, and the representation size of the subsets is reduced to the theoretical lower limit. Moreover, the zeros-based and poles-based secure representation of the subset are used to decide Pos-and-Neg membership, respectively. We further verify the feasibility of combining these two secure representations of the subset, so this result is used to construct our dual membership decision cryptosystem. Specifically, our ZKDMP protocol is proposed for dual membership decisions, which can realize a cryptographic proof of strict Pos-and-Neg membership simultaneously. Furthermore, the zero-knowledge property of our construction ensures that the information of the tested element will not be leaked during the implementation of the protocol. In addition, we provide detailed security proof of our ZKDMP protocol, including positive completeness, negative completeness, soundness and zero-knowledge.
Emanuele Raso, Lorenzo Bracciale, Pierluigi Gallo, Giorgio Bernardinetti ¡ 7 authors
Blockchain technology can be applied to smart grids to support business and management operations, and its distributed nature is advantageous when considering user-managed renewable systems. However, these new fully distributed systems raise a number of security and privacy issues. For this reason, numerous solutions integrating blockchain with privacy and security enhancing technologies, such as homomorphic encryption, secret sharing and zero-knowledge proof, have been proposed in the literature. The complexity of such systems from an algorithmic and protocol point of view is obvious, while the computational cost is less obvious and often overlooked by the authors. In this paper, we want to experimentally evaluate the computational weight of cryptographic techniques proposed to guarantee security and privacy on blockchains. We will take as reference commercial devices with computational capabilities similar to those we expect to find in smart grid devices. The results show that some techniques are not suitable for these scenarios and that architectural solutions must therefore be carefully designed.
Xin Liu Xin Liu, Yang Xu Xin Liu, Gang Xu Yang Xu, Xiu-Bo Chen Gang Xu ¡ 5 authors
<p>With the rapid development of the Internet and information technology, the problem of zero-trust networks has become increasingly prominent, and secure multi-party computation has become a research hotspot to solve the problem of zero-trust networks. The secure judgment of point and line relationship is an important research branch of secure computing set geometry. However, most of resent secure computing protocols of point and line relationship are designed in the semi-honest model and cannot resist malicious attacks. Therefore, this paper analyzes the possible malicious adversary behaviors and designs a secure protocol in the malicious model. In this paper, the Paillier cryptosystem, zero- knowledge proof, and cut-choose method are used to resist malicious behavior, and the real/ideal model paradigm method is used to prove the security of the protocol. Compared with the existing solutions, the malicious model protocol is still efficient and widely used in real applications.</p> <p>&nbsp;</p>
Cryptography has proven to be one of the most contentious areas in modern society. For some it protects the rights of individuals to privacy and security, while for others it puts up barriers against the protection of our society. This book aims to develop a deep understanding of cryptography, and provide a way of understanding how privacy, identity provision and integrity can be enhanced with the usage of encryption. The book has many novel features including: ⢠full provision of Web-based material on almost every topic covered ⢠provision of additional on-line material, such as videos, source code, and labs ⢠coverage of emerging areas such as Blockchain, Light-weight Cryptography and Zero-knowledge Proofs (ZKPs) Key areas covered include: ⢠Fundamentals of Encryption ⢠Public Key Encryption ⢠Symmetric Key Encryption ⢠Hashing Methods ⢠Key Exchange Methods ⢠Digital Certificates and Authentication ⢠Tunneling ⢠Crypto Cracking ⢠Light-weight Cryptography ⢠Blockchain ⢠Zero-knowledge Proofs This book provides extensive support through the associated website of: http://asecuritysite.com/encryption
As IoT becomes omnipresent vast amounts of data are generated, which can be used for building innovative applications. However,interoperability issues and security concerns, prevent harvesting the full potentials of these data. In this paper we consider the use case of data generated by smart buildings. Buildings are becoming ever "smarter" by integrating IoT devices that improve comfort through sensing and automation. However, these devices and their data are usually siloed in specific applications or manufacturers, even though they can be valuable for various interested stakeholders who provide different types of "over the top" services, e.g., energy management. Most data sharing techniques follow an "all or nothing" approach, creating significant security and privacy threats, when even partially revealed, privacy-preserving, data subsets can fuel innovative applications. With these in mind we develop a platform that enables controlled, privacy-preserving sharing of data items. Our system innovates in two directions: Firstly, it provides a framework for allowing discovery and selective disclosure of IoT data without violating their integrity. Secondly, it provides a user-friendly, intuitive mechanisms allowing efficient, fine-grained access control over the shared data. Our solution leverages recent advances in the areas of Self-Sovereign Identities, Verifiable Credentials, and Zero-Knowledge Proofs, and it integrates them in a platform that combines the industry-standard authorization framework OAuth 2.0 and the Web of Things specifications.
Felix Engelmann, Thomas Kerber, Markulf Kohlweiss, Mikhail Volkhov
Privacy-oriented cryptocurrencies, like Zcash or Monero, provide fair transaction anonymity and confidentiality, but lack important features compared to fully public systems, like Ethereum. Specifically, supporting assets of multiple types and providing a mechanism to atomically exchange them, which is critical for e.g. decentralized finance (DeFi), is challenging in the private setting. By combining insights and security properties from Zcash and SwapCT (PETS 21, an atomic swap system for Monero), we present a simple zk-SNARKs based transaction scheme, called Zswap, which is carefully malleable to allow the merging of transactions, while preserving anonymity. Our protocol enables multiple assets and atomic exchanges by making use of sparse homomorphic commitments with aggregated open randomness, together with Zcash friendly simulation-extractable non-interactive zero-knowledge (NIZK) proofs. This results in a provably secure privacypreserving transaction protocol, with efficient swaps, and overall performance close to that of existing deployed private cryptocurrencies. It is similar to Zcash Sapling and benefits from existing code-bases and implementation expertise.
Foteini Baldimtsi, Panagiotis Chatzigiannis, Steven Gordon, Phi Hung Le ¡ 5 authors
We present gOTzilla, a protocol for interactive zero-knowledge proofs for very large disjunctive statements of the following format: given publicly known circuit C, and set of values Y = {y1 , . . . , yn }, prove knowledge of a witness x such that C(x) = y1 ⨠C(x) = y2 ⨠¡ ¡ ¡ ⨠C(x) = yn . These type of statements are extremely important for the proof of assets (PoA) problem in cryptocurrencies where a prover wants to prove the knowledge of a secret key sk that associates with the hash of a public key H(pk) posted on the ledger. We note that the size of n in popular cryptocurrencies, such as Bitcoin, is estimated to 80 million. For the construction of gOTzilla, we start by observing that if we restructure the proof statement to an equivalent of proving knowledge of (x, y) such that (C(x) = y) ⧠(y = y1 ⨠¡ ¡ ¡ ⨠y = yn )), then we can reduce the disjunction of equalities to 1-out-of-N oblivious transfer (OT). Our overall protocol is based on the MPC in the head (MPCitH) paradigm. We additionally provide a concrete, efficient extension of our protocol for the case where C combines algebraic and non-algebraic statements (which is the case in the PoA application). We achieve an asymptotic communication cost of O(log n) plus the proof size of the underlying MPCitH protocol. While related work has similar asymptotic complexity, our approach results in concrete performance improvements. We implement our protocol and provide benchmarks. Concretely, for a set of size 1 million entries, the total run-time of our protocol is 14.89 seconds using 48 threads, with 6.18 MB total communication, which is about 4x faster compared to the state of the art when considering a disjunctive statement with algebraic and non-algebraic elements.
Uk Jo, Yustus Eko Oktian, Donggyu Kim, Sangbong Oh ¡ 6 authors
Contact tracing is an effective strategy to slow down the COVID-19 pandemic. However, the use of digital footprints as supportive evidences in the contact tracing process rises the privacy problems since private information must be shared to the contact tracing providers. This paper proposed a novel privacy-preserving contact tracing procedure based on zero-knowledge-range-proof and blockchain platform, which helps users to prove whether they in contact with the confirmed patient without disclosing the exact location they have visited. The blockchain is used to guarantee anonymity through the use of address as an identity, and provide strong non-repudiation from transactions. Finally, we provide a proof-of-concept implementation of our proposal using Hyperledger Fabric and smartphone application. The evaluation showed that the proposed system can work as intended with minimal processing delay.
Payment channel network (PCN) is a layer-two scaling solution that enables fast off-chain transactions but does not involve on-chain transaction settlement. PCNs raise new privacy issues including balance secrecy, relationship anonymity and payment privacy. Moreover, protecting privacy causes low transaction success rates. To address this dilemma, we propose zk-PCN, a privacy-preserving payment channel network using zk-SNARKs. We prevent from exposing true balances by setting up \textit{public balances} instead. Using public balances, zk-PCN can guarantee high transaction success rates and protect PCN privacy with zero-knowledge proofs. Additionally, zk-PCN is compatible with the existing routing algorithms of PCNs. To support such compatibility, we propose zk-IPCN to improve zk-PCN with a novel proof generation (RPG) algorithm. zk-IPCN reduces the overheads of storing channel information and lowers the frequency of generating zero-knowledge proofs. Finally, extensive simulations demonstrate the effectiveness and efficiency of zk-PCN in various settings.
Differential Privacy (DP) is often presented as a strong privacy-enhancing technology with broad applicability and advocated as a de facto standard for releasing aggregate statistics on sensitive data. However, in many embodiments, DP introduces a new attack surface: a malicious entity entrusted with releasing statistics could manipulate the results and use the randomness of DP as a convenient smokescreen to mask its nefariousness. Since revealing the random noise would obviate the purpose of introducing it, the miscreant may have a perfect alibi. To close this loophole, we introduce the idea of Interactive Proofs For Differential Privacy, which requires the publishing entity to output a zero knowledge proof that convinces an efficient verifier that the output is both DP and reliable. Such a definition might seem unachievable, as a verifier must validate that DP randomness was generated faithfully without learning anything about the randomness itself. We resolve this paradox by carefully mixing private and public randomness to compute verifiable DP counting queries with theoretical guarantees and show that it is also practical for real-world deployment. We also demonstrate that computational assumptions are necessary by showing a separation between information-theoretic DP and computational DP under our definition of verifiability.
Movsowitz Davidow, Danielle, Manevich, Yacov, Toch, Eran
Differential Privacy (DP) is often presented as a strong privacy-enhancing technology with broad applicability and advocated as a de-facto standard for releasing aggregate statistics on sensitive data. However, in many embodiments, DP introduces a new attack surface: a malicious entity entrusted with releasing statistics could manipulate the results and use the randomness of DP as a convenient smokescreen to mask its nefariousness. Since revealing the random noise would obviate the purpose of introducing it, the miscreant may have a perfect alibi. To close this loophole, we introduce the idea of \textit{Verifiable Differential Privacy}, which requires the publishing entity to output a zero-knowledge proof that convinces an efficient verifier that the output is both DP and reliable. Such a definition might seem unachievable, as a verifier must validate that DP randomness was generated faithfully without learning anything about the randomness itself. We resolve this paradox by carefully mixing private and public randomness to compute verifiable DP counting queries with theoretical guarantees and show that it is also practical for real-world deployment. We also demonstrate that computational assumptions are necessary by showing a separation between information-theoretic DP and computational DP under our definition of verifiability.
George Morris William Tangka, Ellie Ophelia Delviolin, Hsien-Ming Chou
E-commerce plays a significant role in a country's economic condition. Since the COVID-19 outbreak, it has become more popular, along with concerns about its ability to handle information security. The Zero-Knowledge Proof (ZKP) method could be a possible solution to the e-commerce payment security issue that hampers customer trust. This paper investigates the viability of an online payment framework based on the ZPK method. This method is an upgrade for authentication during the payment process in online shopping. Experiments on customers' perspectives of the payment framework based on the ZKP method were conducted and supported the perceived usefulness, ease of use, trust, control, satisfaction, and loyalty aspects of a better e-commerce website. It allows advantages for both customers and e-commerce and prevents fraud, which will increase the trust level for both sides. zkSNARK speeds up and lowers the cost of the process, but there is a risk of DOS. Future work needs to be done to handle DOS in this method.
In the race toward next-generation systems of systems, the adoption of edge and cloud computing is escalating to deliver the underpinning end-to-end services. To safeguard the increasing attack landscape, remote attestation lets a verifier reason about the state of an untrusted remote prover. However, for most schemes, verifiability is only established under the omniscient and trusted verifier assumption, where a verifier knows the proverâs trusted states, and the prover must reveal evidence about its current state. This assumption severely challenges upscaling, inherently limits eligible verifiers, and naturally prohibits adoption in public-facing security-critical networks. To meet current zero trust paradigms, we propose a general ZEro-Knowledge pRoof of cOnformance (ZEKRO) scheme, which considers mutually distrusting participants and enables a prover to convince an untrusted verifier about its stateâs correctness in zero-knowledge, i.e., without revealing anything about its state.
In recent years, decentralized applications such as Distributed Ledger Technologies and blockchain have evolved as suitable applications for secure sharing of information in a decentralized fashion using privacy preserving techniques like zero-knowledge protocols. However, the biggest issue with the traditional zero-knowledge protocols on a blockchain ledger is their slow performance on big data. This paper presents the advance zero-knowledge ledger by replacing their range-proof technique with the most efficient range-proof technique based on the improved inner product based zero-knowledge proofs. Moreover, this technique allows the aggregation of multiple range-proofs into a single range-proof, which makes the current zero-knowledge ledger system more efficient than the existing one.
AbstractâThis white paper explores the transformative potential of decentralized AI agents operating within the Web3 infrastructure to enhance user experiences through personalized and privacy- preserving browsing. We investigate how these AI agents can autonomously navigate the web, utilizing smart contracts for automated decision-making processes that prioritize user preferences and privacy. The paper outlines a robust technical framework that includes decentralized AI models running on blockchain networks, integration with existing Web3 protocols such as IPFS and ENS, and the implementation of privacy- preserving AI computation techniques, including zero-knowledge proofs. We present various use cases, including AI-powered decentralized search engines, autonomous content curation and recommendation systems, and smart contract-based content verification and fact- checking mechanisms. Additionally, we address the challenges associated with scalability of AI computation on blockchain, data privacy and sovereignty, token economics for AI services, and governance models for decentralized AI systems. The future impact of these developments on user interfaces in Web3, the democratization of AI services, and the emergence of new business models for decentralized AI is also discussed. This topic is particularly relevant as it addresses current limitations in Web3 user experience, combines two major technological trends, and has practical applications for both users and developers, while exploring novel economic models that contribute to the broader discussion of a decentralized internet. Keywords- Decentralized AI, Web3, Privacy, Smart Contracts, Blockchain, User Experience Index TermsâDecentralized AI, Web3, Privacy, Smart Con- tracts, Blockchain, User Experience
In order to protect data privacy in the context of big data, a new scheme to protect data privacy and user privacy is proposed. This study believes that blockchain technology can be used to build a massive private data retrieval and sharing platform, considering the realization of privacy protection under horizontal federated learning and vertical federated learning, combined with privacy computing technology, to verify the results of massive data retrieval, and to build a "data availability that is not available." The "visible" security reduction model involves three cryptographic algorithms, namely functional encryption, zero-knowledge proof and asymmetric encryption. By combining blockchain and cloud servers, a hybrid storage architecture with data storage on the chain and off-chain storage is realized. And use function encryption and zero-knowledge proof to achieve privacy protection and secure data sharing of verifiable results. The final experimental results prove that the security feasibility of this model is proposed in this paper, which can fully improve the level of data log security management, operation and maintenance supervision, and the quality and efficiency of data security protection, and improve the network security protection system of the power grid in all scenarios.
Abeer AlSereidi, Sarah Qahtan, R. T. Mohammed, A. A. Zaidan ¡ 17 authors
Context: When the epidemic first broke out, no specific treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment efforts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The first is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes five sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates their weights using the newly formulated interval-valued spherical fuzzy and hesitant 2-tuple fuzzy-weighted zero-inconsistency (IVSH2-FWZIC), and allocates their values. The subsequent phases are federation, dose availability confirmation, global prioritization of eligible patients and alerting the hospitals with the patients most eligible for receiving the anti-SARS-CoV-2 mAbs according to dose availability. The LM independently performs all local prioritization processes without sharing patientsâ data using the provided criteria settings and federated parameters from the CFS via the proposed Federated TOPSIS (F-TOPSIS). The sequential processing steps are coherently performed at both sides. Results and Discussion: (1) The proposed FDMD efficiently and independently identifies the high-risk patients most eligible for receiving anti-SARS-CoV-2 mAbs at each local distribution hospital. The final decision at the CFS relies on the indexed patientsâ score and dose availability without sharing the patientsâ data. (2) The IVSH2-FWZIC effectively weighs the high-risk criteria of patients with SARS-CoV-2. (3) The local and global prioritization ranks of the F-TOPSIS for eligible patients are subjected to a systematic ranking validated by high correlation results across nine scenarios by altering the weights of the criteria. (4) A comparative analysis of the experimental results with a prior study confirms the effectiveness of the proposed FDMD. Conclusion: The proposed FDMD has the benefits of centrally distributing anti-SARS-CoV-2 mAbs to high-risk patients prioritized based on their eligibility and dose availability, and simultaneously protecting their privacy and offering an effective cure to prevent progression to severe SARS-CoV-2 hospitalization or death.
In this paper, we propose a blockchain-based collaborative credential management scheme for anonymous authentication in space-air-ground integrated vehicular networks (SAGVN), namedSAG-BC. First, we build a consortium blockchain among service providers and design a distributed system setup (DSS) scheme to securely generate public parameters for issuing credentials. Second, we design a collaborative credential issuance (CCI) scheme to generate a succinct and easy-to-manage subscription credential. The credential can be used by users to access different access points in SAGVN efficiently without revealing true identities from the authentication messages. With co-designs of zero-knowledge proofs and succinct on-chain commitments,SAG-BCprovides efficient verifiability and incentives for credential management operations in SAGVN. By doing so, expensive on-chain storage and computational overheads are reduced in the DSS and CCI. Finally, we conduct a thorough security analysis to demonstrate thatSAG-BCachieves security and verifiability for credential management in SAGVN. We set up a real-world blockchain network and conduct extensive experiments to show the feasibility and efficiency ofSAG-BC.
Weiqi Dai, Shuyue Tuo, Liang Yu, KimâKwang Raymond Choo ¡ 6 authors
Data is a key asset in our interconnected and smart city. Especially, in the context of healthcare, healthcare data can facilitate remote diagnosis and medical research. Because of the potentially sensitive nature of healthcare data, privacy is a key consideration for both individuals and organizations. We can broadly categorize privacy considerations into data privacy, attribute privacy, and privilege policy privacy. To support one or more notions of privacy, the potential of solutions, such as fine-grained access control [e.g., those based on attribute-based encryption (ABE)] and blockchain in realizing data sharing has been explored. However, these approaches generally only facilitate access control of data and the traceability of the sharing process, and do not protect the attribute and privilege policy privacy of users. Therefore, in this article, we implement HAPPS, a hidden attribute and privilege-protection data-sharing scheme with verifiability. The three key building blocks of HAPPS are zero-knowledge proof, blockchain, and distributed ABE (DABE). Specifically, in our approach, we propose a new data access control strategy (i.e., attribute-hidden zero-knowledge proofâat-ZKP) to hide user identity and attributes during the authorization process. Our scheme is embedded in the blockchain and built into the decentralized sharing platform to prevent central verifier counterfeiting and support auditing. To demonstrate utility, we prove that HAPPS ensures data, attribute, and privilege policy privacy. Findings of our evaluations implemented on Ethereum and using the data set from the healthcare cost and utilization project (HCUP), we demonstrate that our scheme can share sensitive healthcare records belonging to minors (e.g., children) without the at-ZKP incurring unrealistic cost.
Yang Yang, Shangbin Han, Ping Xie, Yan Zhu ¡ 8 authors
With the increasing demand for privacy protection in the blockchain, the universal zero-knowledge proof protocol has been developed and widely used. Because hash function is an important cryptographic primitive in a blockchain, the zero-knowledge proof of hash preimage has a wide range of application scenarios. However, it is hard to implement it due to the transformation of efficiency and execution complexity. Currently, there are only zero-knowledge proof circuits of some widely used hash functions that have been implemented, such as SHA256. SM3 is a Chinese hash function standard published by the Chinese Commercial Cryptography Administration Office for the use of electronic authentication service systems, and hence might be used in several cryptographic applications in China. As the national cryptographic hash function standard, the zero-knowledge proof circuit of SM3 (Chinese Commercial Cryptography) has not been implemented. Therefore, this paper analyzed the SM3 algorithm process, designed a new layered circuit structure, and implemented the SM3 hash preimage zero-knowledge proof circuit with a circuit size reduced by half compared to the automatic generator. Moreover, we proposed several extended practical protocols based on the SM3 zero-knowledge proof circuit, which is widely used in blockchain.
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 â16) 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 â21) 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 â01). 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.