Zero-knowledge Proofs Research Articles

PDMSC: privacy-preserving decentralized multi-skill spatial crowdsourcing

Purpose The purpose of this paper is to study the following two issues regarding blockchain crowdsourcing. First, to design smart contracts with lower consumption to meet the needs of blockchain crowdsourcing services and also need to design better interaction modes to further reduce the cost of blockchain crowdsourcing services. Second, to design an effective privacy protection mechanism to protect user privacy while still providing high-quality crowdsourcing services for location-sensitive multiskilled mobile space crowdsourcing scenarios and blockchain exposure issues. Design/methodology/approach This paper proposes a blockchain-based privacy-preserving crowdsourcing model for multiskill mobile spaces. The model in this paper uses the zero-knowledge proof method to make the requester believe that the user is within a certain location without the user providing specific location information, thereby protecting the user’s location information and other privacy. In addition, through off-chain calculation and on-chain verification methods, gas consumption is also optimized. Findings This study deployed the model on Ethereum for testing. This study found that the privacy protection is feasible and the gas optimization is obvious. Originality/value This study designed a mobile space crowdsourcing based on a zero-knowledge proof privacy protection mechanism and optimized gas consumption.

Privacy-Preserving Location Authentication for Low-altitude UAVs: A Blockchain-based Approach

Efficient and trusted regulation of unmanned aerial vehicles (UAVs) is an essential but challenging issue in the future era of Internet of Low-altitude Intelligence, due to the difficulties in UAVs' identity recognition and location matching, potential for falsified information reporting, etc. To address this challenging issue, in this paper, we propose a blockchain-based UAV location authentication scheme, which employs a distance bounding protocol to establish a location proof, ensuring the authenticity of UAV positions. To preserve the privacy of UAVs, anonymous certificates and zero-knowledge proof are used. The security of the proposed scheme is analyzed. Experiments demonstrate the efficiency and feasibility of the proposed scheme.

E-Coin-Based Priced Oblivious Transfer with a Fast Item Retrieval

Priced oblivious transfer (POT) is a cryptographic protocol designed for privacy-preserving e-commerce of digital content. It involves two parties: the merchant, who provides a set of priced items as input, and a customer, who acquires one of them. After the protocol has run, the customer obtains the item they chose, while the merchant cannot determine which one. Moreover, the protocol guarantees that the customer gets the content only if they have paid the price established by the merchant. In a recent paper, the authors proposed a POT system where the payments employed e-coin transactions. The strong point of the proposal was the absence of zero-knowledge proofs required in preceding systems to guarantee the correctness of payments. In this paper, we propose a novel e-coin-based POT system with a fast item retrieval procedure whose running time does not depend on the number of items for sale. This is an improvement over the aforementioned existing proposal whose execution time becomes prohibitively long when the catalog is extensive. The use of zero-knowledge proofs is neither required.

Load-Balanced Parallel Implementation on GPUs for Multi-Scalar Multiplication Algorithm

Multi-scalar multiplication (MSM) is an important building block in most of elliptic-curve-based zero-knowledge proof systems, such as Groth16 and PLONK. Recently, Lu et al. proposed cuZK, a new parallel MSM algorithm on GPUs. In this paper, we revisit this scheme and present a new GPU-based implementation to further improve the performance of MSM algorithm. First, we propose a novel method for mapping scalars into Pippenger’s bucket indices, largely reducing the number of buckets compared to the original Pippenger algorithm. Second, in the case that memory is sufficient, we develop a new efficient algorithm based on homogeneous coordinates in the bucket accumulation phase. Moreover, our accumulation phase is load-balanced, which means the parallel speedup ratio is almost linear growth as the number of device threads increases. Finally, we also propose a parallel layered reduction algorithm for the bucket aggregation phase, whose time complexity remains at the logarithmic level of the number of buckets. The implementation results over the BLS12-381 curve on the V100 graphics card show that our proposed algorithm achieves up to 1.998x, 1.821x and 1.818x speedup compared to cuZK at scales of 221, 222, and 223, respectively.

Secure Device-to-Device Communication in IoT: Fuzzy Identity from Wireless Channel State Information for Identity-Based Encryption

With the rapid development of the Internet of Things (IoT), ensuring secure communication between devices has become a crucial challenge. This paper proposes a novel secure communication solution by extracting wireless channel state information (CSI) features from IoT devices to generate a device identity. Due to the instability of the wireless channel, the CSI features are fuzzy and time-varying; thus, we a employ locally sensitive hashing (LSH) algorithm to ensure the stability of the generated identity in a dynamically changing wireless channel environment. Furthermore, zero-knowledge proofs are utilized to guarantee the authenticity and effectiveness of the generated identity. Finally, the identity generated using the aforementioned approach is integrated into an IBE communication scheme, which involves the fuzzy extraction of channel state information from IoT devices, stable identity extraction for fuzzy IoT devices using LSH, and the use of zero-knowledge proofs to ensure the authenticity of the generated identity. This identity is then employed as the identity information in identity-based encryption (IBE), constructing the device’s public key for achieving confidential communication between devices.

Secure decentralized firmware update delivery service for Internet of Things

With the increasing number of Internet of Things devices, it is crucial to keep them up-to-date to prevent cyber attacks. Traditional centralized delivery is not suitable for scaling and also can be too expensive to run for small vendors. Thus, finding a fully secure, scalable, yet cost-efficient firmware update distribution strategy is always an open research problem. This paper tries to answer those problems by proposing Patchman, a secure decentralized firmware update delivery service for the Internet of Things ecosystem leveraging blockchain. When a new firmware patch is available, vendors make a bid in the smart contract for anyone to join as firmware distributors. For each successful delivery to targeted devices, distributors are rewarded with tokens. Meanwhile, devices gain a reputation score every time they successfully install an update. To ensure robustness and fairness, we develop secure fair exchange protocols using verifiable proof-of-delivery and proof-of-installation. Those proofs can be traded in the blockchain for rewards and reputation scores increase, proving that the proof-holders have successfully processed a firmware delivery and firmware installation task. This way, the firmware update delivery can be executed safely without centralized third-party control. Our evaluation results show that our implementation complies with the five security goals that we envision. We also have successfully punished malicious actions by confiscating their deposits and requiring them to pay up to four times of base deposit value when they join the next update task, ensuring the fairness of our protocol. Furthermore, we generate low processing delay overhead compared to existing works that rely on Zero-Knowledge Proofs. The gas usage consumption from our approach also produced a competitive result despite our works supporting more features than existing works, ensuring the efficiency of our proposal.

Blockchain-Based Electronic Voting System: Significance and Requirements

In a democratic regime, voting is crucial to making collective decisions. Unfortunately, although this activity has great significance and value, little effort has been made to improve the way we vote. Paper ballots are still the most used method, although this method is relatively simple, brings many inconveniences, and represents a contradiction to the modern world and its advances. This paper mostly focuses on a review study of blockchain-based voting systems. It aims at identifying the strategies and the guidelines as well as provides a comprehensive end-to-end electronic voting system based on blockchain, with the help of cryptographic techniques such as zero-knowledge proofs to improve privacy. The novelty of this paper is that we tackle the limitations of electronic voting systems found in the literature, including cost, identity management, and scalability problems. Our purpose is to provide key elements for organizations on how to design their proper electronic voting system based on blockchain technology.

Privacy computing meets metaverse: Necessity, taxonomy and challenges

Metaverse, the core of the next-generation Internet, is a computer-generated holographic digital environment that simultaneously combines spatio-temporal, immersive, real-time, sustainable, interoperable, and data-sensitive characteristics. It cleverly blends the virtual and real worlds, allowing users to create, communicate, and transact in virtual form. With the rapid development of emerging technologies including augmented reality, virtual reality and blockchain, the metaverse system is becoming more and more sophisticated and widely used in various fields such as social, tourism, industry and economy. However, the high level of interaction with the real world also means a huge risk of privacy leakage both for individuals and enterprises, which has hindered the wide deployment of metaverse. Then, it is inevitable to apply privacy computing techniques in the framework of metaverse, which is a current research hotspot. In this paper, we conduct comprehensive research on the necessity, taxonomy and challenges when privacy computing meets metaverse. Specifically, we first introduce the underlying technologies and various applications of metaverse, on which we analyze the challenges of data usage in metaverse, especially data privacy. Next, we review and summarize state-of-the-art solutions based on federated learning, differential privacy, homomorphic encryption, and zero-knowledge proofs for different privacy problems in metaverse. Finally, we show the current security and privacy challenges in the development of metaverse and provide open directions for building a well-established privacy-preserving metaverse system. For easy access and reference, we integrate the related publications and their codes into a GitHub repository: .

A blockchain privacy-conserving framework for secure medical data transmission in the internet of medical things

The Internet of Medical Things (IoMT) has transformed healthcare, collecting and transmitting vast medical data. This study proposes an innovative solution, integrating blockchain into IoMT within a fog-cloud computing framework for secure medical data transmission. The blockchain-based zero-trust system ensures reliable data auditing and Electronic Health Repository (EHR) protection. New blockchain entries atop prior blocks deter tampering, with Quad Merkle tree and zero-knowledge proof encryption ensuring data integrity and privacy. The multi-critic deep deterministic policy gradient algorithm optimizes task-offloading decisions in the fog-cloud layer. Security analyses validate its effectiveness, improving computing efficiency and ensuring data fidelity and privacy. These findings position the system as a promising solution for enhancing medical data security and integrity in edge-fog cloud environments, aligning with evolving healthcare technology demands.

A comparative study between zero-knowledge proof (ZKP) and ring signatures targeting the legal and regulatory implications with the general data protection law (GDPL) and general data protection regulation (GDPR)

Privacy protection ensures that individuals have control over personal data, preventing abuse and preserving trust in the use of online services. In the “Digital Era”, where the collection, storage and processing of personal information have become ubiquitous, data privacy emerges as a relevant topic. In this sense, laws were created, such as the General Data Protection Law (LGPD) in Brazil and the General Data Protection Regulation (GDPR) in Europe, to control privacy and the processing of personal data. The article presents a comparative analysis of 2 (two) data privacy mechanisms, the Zero-Knowledge Proof (ZKP) and Ring Signatures, used in Blockchain, aiming at the legal and regulatory implications with the LGPD and GDPR. The comparative study between ZKP and Ring Signatures highlights the flexibility of ZKP in various contexts, including voting and secure authentication systems, while Ring Signatures offer significant advantages in terms of scalability and efficiency in systems where subscriber anonymity is considered fundamental. Furthermore, the legal and regulatory implications of the ZKP are discussed, mainly in relation to LGPD and GDPR. Finally, the article concludes that the comparative analysis offers insights into applications, challenges and legal and regulatory implications, particularly in relation to data privacy and compliance with regulations such as LGPD and GDPR.

Enhancing Industrial IoT Network Security through Blockchain Integration

In the rapidly evolving landscape of industrial ecosystems, Industrial IoT networks face increasing security challenges. Traditional security methods often struggle to protect these networks adequately, posing risks to data integrity, confidentiality, and access control. Our research introduces a methodology that leverages blockchain technology to enhance the security and trustworthiness of IoT networks. This approach starts with sensor nodes collecting and compressing data, followed by encryption using the ChaCha20-Poly1305 algorithm and transmission to local aggregators. A crucial element of our system is the private blockchain gateway, which processes and classifies data based on confidentiality levels, determining their storage in cloud servers or the Interplanetary File System for enhanced security. The system’s integrity and authenticity are further reinforced through the proof of authority consensus mechanism. This system employs Zero Knowledge Proof challenges for device authorization, optimizing data retrieval while maintaining a delicate balance between security and accessibility. Our methodology contributes to mitigating vulnerabilities in Industrial IoT networks and is part of a broader effort to advance the security and operational efficiency of these systems. It reflects an understanding of the diverse and evolving challenges in IoT security, emphasizing the need for continuous innovation and adaptation in this dynamic field.

Zero-knowledge proofs in education: a pathway to disability inclusion and equitable learning opportunities

In the evolving landscape of global education, the significance of inclusivity and equity has never been more important. Emphasizing the United Nation Sustainable Development Goal 4, this paper explores the innovative application of blockchain-powered Zero-Knowledge Proofs (ZKPs) technology in education, with a particular focus on disability inclusion. This study introduces a novel disability management system powered by Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK). This advanced system enables educational institutions to verify the status of students with disabilities without compromising their personal information, thereby preserving their privacy and reinforcing their identity. This paper evaluates the potential operational efficiency of this prototype system against the existing costs incurred by higher education institutions in disability schemes. It also examines the system’s potential to enhance self-disclosure among students with disability, which is pivotal for their academic success. By advocating for privacy and inclusivity, this study highlights the transformative potential of ZKP in creating an educational environment where students with disabilities can comfortably disclose their needs. This approach not only protects their confidentiality but also empowers them academically, aligning with the global commitment to accessible and inclusive education.

Scalability meets regulation: UTXO-based sharding and zero-knowledge proofs for regulated digital currencies

Scalability meets regulation: UTXO-based sharding and zero-knowledge proofs for regulated digital currencies

Armed with Faster Crypto: Optimizing Elliptic Curve Cryptography for ARM Processors

Elliptic curve cryptography is a widely deployed technology for securing digital communication. It is the basis of many cryptographic primitives such as key agreement protocols, digital signatures, and zero-knowledge proofs. Fast elliptic curve cryptography relies on heavily optimised modular arithmetic operations, which are often tailored to specific micro-architectures. In this article, we study and evaluate optimisations of the popular elliptic curve Curve25519 for ARM processors. We specifically target the ARM NEON single instruction, multiple data (SIMD) architecture, which is a popular architecture for modern smartphones. We introduce a novel representation for 128-bit NEON SIMD vectors, optimised for SIMD parallelisation, to accelerate elliptic curve operations significantly. Leveraging this representation, we implement an extended twisted Edwards curve Curve25519 back-end within the popular Rust library “curve25519-dalek”. We extensively evaluate our implementation across multiple ARM devices using both cryptographic benchmarks and the benchmark suite available for the Signal protocol. Our findings demonstrate a substantial back-end speed-up of at least 20% for ARM NEON, along with a noteworthy speed improvement of at least 15% for benchmarked Signal functions.

Zk-SNARKs-Based Anonymous Payment Channel in Blockchain

Payment channels serve as an effective solution to the scalability problem of cryptocurrencies, which significantly increase transaction rates by allowing users to conduct large-scale offline transactions off-chain without posting everything to the blockchain. However, the existing payment channels lack privacy protection for the transaction amount and the linking relationship between the two parties to the transaction. Therefore, in order to address the scalability and privacy issues of cryptocurrencies such as Bitcoin, this paper proposes a zk-SNARKs-based anonymous payment channel (zk-APC), which supports an unlimited number of off-chain payments between the payer and the payee and protects the privacy of the participants. Specifically, the proposed scheme achieves relational anonymity and amount privacy for both on-chain and off-chain transactions in the payment channel through utilizing zero-knowledge proof (zk-SNARKs) and commitment schemes. This paper proves that the proposed method is more effective than similar schemes through a performance evaluation.

Sphinx-in-the-Head: Group Signatures from Symmetric Primitives

Group signatures and their variants have been widely used in privacy-sensitive scenarios such as anonymous authentication and attestation. In this paper, we present a new post-quantum group signature scheme from symmetric primitives. Using only symmetric primitives makes the scheme less prone to unknown attacks than basing the design on newly proposed hard problems whose security is less well-understood. However, symmetric primitives do not have rich algebraic properties, and this makes it extremely challenging to design a group signature scheme on top of them. It is even more challenging if we want a group signature scheme suitable for real-world applications, one that can support large groups and require few trust assumptions. Our scheme is based on MPC-in-the-head non-interactive zero-knowledge proofs, and we specifically design a novel hash-based group credential scheme, which is rooted in the SPHINCS+ signature scheme but with various modifications to make it MPC (multi-party computation) friendly. The security of the scheme has been proved under the fully dynamic group signature model. We provide an implementation of the scheme and demonstrate the feasibility of handling a group size as large as 2 60 . This is the first group signature scheme from symmetric primitives that supports such a large group size and meets all the security requirements.

Enhancing Security and Flexibility in the Industrial Internet of Things: Blockchain-Based Data Sharing and Privacy Protection

To address the complexities, inflexibility, and security concerns in traditional data sharing models of the Industrial Internet of Things (IIoT), we propose a blockchain-based data sharing and privacy protection (BBDSPP) scheme for IIoT. Initially, we characterize and assign values to attributes, and employ a weighted threshold secret sharing scheme to refine the data sharing approach. This enables flexible combinations of permissions, ensuring the adaptability of data sharing. Subsequently, based on non-interactive zero-knowledge proof technology, we design a lightweight identity proof protocol using attribute values. This protocol pre-verifies the identity of data accessors, ensuring that only legitimate terminal members can access data within the system, while also protecting the privacy of the members. Finally, we utilize the InterPlanetary File System (IPFS) to store encrypted shared resources, effectively addressing the issue of low storage efficiency in traditional blockchain systems. Theoretical analysis and testing of the computational overhead of our scheme demonstrate that, while ensuring performance, our scheme has the smallest total computational load compared to the other five schemes. Experimental results indicate that our scheme effectively addresses the shortcomings of existing solutions in areas such as identity authentication, privacy protection, and flexible combination of permissions, demonstrating a good performance and strong feasibility.

Enabling Data Sharing Through Data Trusts in LEO Satellite Internet

Due to the advantages of low latency, small propagation loss, and global coverage, LEO satellite network becomes a cost-effective long-distance communication solution for IoT devices in remote areas. Nevertheless, the LEO satellite IoT devices deployed for specific tasks are idle most of the time, raising the obvious question for future research: how to effectively share these idle sensing resources. Fortunately, data trusts emerge as a promising strategy to leverage the idle sensing resources. However, the data trust model faces privacy and security challenges in LEO satellite IoT scenarios to achieve trusted data trading. Therefore, this paper proposes a blockchainenabled data trust model and a trusted data trading scheme for device providers, data trustees, and general users. Furthermore, exploiting the homomorphic encryption technology, we realize the fine-grained data query and device match in the premise of privacy protection for device providers and general users. Lastly, considering the openness of satellite-terrestrial communication links and the limited computing power of LEO satellite IoT devices, we design a non-interactive lightweight authentication protocol based on zero-knowledge proof. The simulation results verify the feasibility, effectiveness, and security performance of the proposed data sharing for LEO satellite IoT.

Efficient First-price Sealed E-auction Protocol Under Secure Multi-party Computational Malicious Model

<p>To solve the problems of existing e-auction protocols such as semi-trustworthiness of outsourced third parties, collusive attacks among participants, unsatisfactory decentralized structure, and inability of public verification, we propose an efficient first-price sealed e-auction protocol under a secure multi-party computational malicious model. First, the protocol combines the additive homomorphism of the ElGamal cryptographic algorithm to achieve a decentralized structure and eliminate the problem of semi-trustworthiness of outsourced third parties; it uses (n, n) threshold encryption and decryption techniques to solve the problem of collusion attacks among participants and uses Hash-based Message Authentication Code (HMAC) technology to achieve public verifiability of auction results. Additionally, the protocol proposes a method to quickly find the maximum value of the data encoding, which can avoid multiple processing of confidential data and thus effectively reduce the number of communication rounds. The combination of zero-knowledge proof and ideal/realistic simulation paradigm proves that the protocol in this paper is resistant to up to n-1 party collusion attacks and satisfies the security of the secure multi-party computational malicious model. Finally, after theoretical analysis and simulation experiments, the protocol not only satisfies higher security performance but also has greater overall operational efficiency.</p> <p> </p>

Integrating Blockchain and Deep Learning Into Extremely Resource-Constrained IoT: An Energy-Saving Zero-Knowledge PoL Approach

The convergence of blockchain and deep learning (DL) drives the intelligence of the Internet of Things (IoT) with security guarantees. However, the soaring resource consumption resulting from blockchain mining and DL model training has overwhelmed the extremely resource-constrained IoT. In this paper, we first build a blockchain and DL-empowered cloud-edge orchestrated framework for an extremely resource-constrained IoT environment. To solve the resource bottleneck of this framework, we then propose a Zero-Knowledge Proof of Learning (ZPoL) consensus approach to channel the meaningless Proof of Work (PoW) mining energy waste to valuable DL model training, while protecting the DL model privacy. Besides, to encourage resource-constrained IoT devices to perform meaningful DL model mining in our ZPoL consensus, we design a model quality-aware incentive mechanism based on a two-stage Stackelberg game. Moreover, we conduct extensive simulations and experiments to evaluate our proposed ZPoL-based framework. The numerical simulation illustrates our proposed incentive mechanism could motivate IoT devices to actively join in DL model mining. Compared with the existing blockchain and DL-enabled IoT system, experimental results demonstrate our proposed ZPoL-based framework could significantly reduce the communication, computation, and storage cost, which is more applicable to a resource-constrained IoT environment.