Collision-resistant, cryptographic hash (CRH) functions have long been an integral part of providing security and privacy in modern systems. Certain constructions of zero-knowledge proof (ZKP) protocols aim to utilize CRH functions to perform cryptographic hashing. Standard CRH functions, such as SHA2, are inefficient when employed in the ZKP domain, thus calling for ZK-friendly hashes, which are CRH functions built with ZKP efficiency in mind. The most mature ZK-friendly hash, MiMC, presents a block cipher and hash function with a simple algebraic structure that is well-suited, due to its achieved security and low complexity, for ZKP applications. Although ZK-friendly hashes have improved the performance of ZKP generation in software, the underlying computation of ZKPs, including CRH functions, must be optimized on hardware to enable practical applications. The challenge we address in this work is determining how to efficiently incorporate ZK-friendly hash functions, such as MiMC, into hardware accelerators, thus enabling more practical applications. In this work, we introduce AMAZE, a highly hardware-optimized open-source framework for computing the MiMC block cipher and hash function. Our solution has been primarily directed at resource-constrained edge devices; consequently, we provide several implementations of MiMC with varying power, resource, and latency profiles. Our extensive evaluations show that the AMAZE-powered implementation of MiMC outperforms standard CPU implementations by more than 13$\times$. In all settings, AMAZE enables efficient ZK-friendly hashing on resource-constrained devices. Finally, we highlight AMAZE's underlying open-source arithmetic backend as part of our end-to-end design, thus allowing developers to utilize the AMAZE framework for custom ZKP applications.
The Riemann hypothesis, renowned for its deep connection to the distribution of prime numbers, remains a central problem in mathematics. Understanding the distribution of primes is crucial for developing efficient algorithms and advancing our knowledge of number theory. The Riemann hypothesis is the assertion that all non-trivial zeros are complex numbers with real part $\frac{1}{2}$. It is considered by many to be the most important unsolved problem in pure mathematics. Several equivalent formulations of the Riemann hypothesis exist. Robin's criterion for the Riemann hypothesis is based on an inequality that divisor sum function $\sigma$ must satisfy at natural numbers greater than 5040. We require the properties of superabundant numbers, that is to say left to right maxima of $n \mapsto \frac{\sigma(n)}{n}$. By using Robin's criterion on superabundant numbers, we present a novel approach that culminates in a complete proof of the Riemann hypothesis. This work is an expansion and refinement of the article "Robin's criterion on divisibility", published in The Ramanujan Journal.
The diversity and scarcity of the medical information makes it difficult to create precise global classification approach for the healthcare applications.The main motive is the privacy issue that restricts the data exchanging scope between healthcare institutions.On the contrary, an information from single source is not adequate for developing the worldwide diagnosis approach.The Federated Learning (FL) is a promising solution for privacy and data multiplicity issues, an appropriate aggregation model for multi class and dissimilar medical information is still challenging task in the recognition.Moreover, the FL approaches does not effectively analyzes the each participant execution in the local model and secures the user data.In order to overcome this issue, the Zero-Knowledge Proof (ZKP) based FL approach is developed over blockchain (BC) for performing the COVID-19 classification.The global model of FL uses the two layer Long Short Term Memory (2LLSTM) with federated proximal term (FedProx) namely 2LLSTMFP while the Convolutional Neural Network (CNN) is used in the local model.The integration ZKP and BS is used to improve the data confidentiality while the immutability of BC helps to prevent unauthorized variations for the ledger.The developed FLBC-ZKP is analyzed with two datasets such as COVID-19 Radiography, and CXR images pneumonia and COVID-19.The FLBC-ZKP is evaluated using accuracy, recall, precision, specificity, F1-score, False Negative Rate (FNR) and False Positive Rate (FPR).The existing researches such as WMT, MCCF, 3SFDL and TOTL are used to compare the FLBC-ZKP method.The FLBC-ZKP achieves improved accuracy of 98.34 % for COVID-19 Radiography dataset that is better than the MCCF and 3SFDL.
In the rapidly expanding field of the Internet of Things, ensuring secure and efficient communication between IoT devices and servers is crucial. This paper henceforth designs a lightweight authentication system under the Zero Knowledge Proof (ZKP) protocol, utilizing the HMAC hashing algorithm for the improvement of security measures put in place. This system is designed to enable the authentication of IoT devices without sending their actual password, thus avoiding risks due to password interception at the point of authentication. For instance, if using the phrase "God is great" as the password, one can get it hashed into HMAC with this method and still be assured that it is secure even if proof is intercepted. Simulations in the simulator shall evaluate the system’s effectiveness and performance. Performance metrics include accuracy, response time, memory consumption, latency, and throughput. The presented results intend to serve lightweight device authentication mechanisms for IoT devices such that the trustworthiness of IoT devices is achieved without hampering their performance. The presented results are also shown to outperform conventional methods.
User Authentication and Security Systems
Advanced Malware Detection Techniques
Advanced Steganography and Watermarking Techniques
This research paper explores the CONIKS key management system’s security and efficiency, a system designed to ensure transparency and privacy in cryptographic operations. We conducted a comprehensive analysis of the underlying mathematical principles, focusing on cryptographic hash functions and digital signature schemes, and their implementation in the CONIKS model. Through the use of Merkle trees, we verified the integrity of the system, while zero-knowledge proofs were utilized to ensure the confidentiality of key bindings. We conducted experimental evaluations to measure the performance of cryptographic operations like key generation, signing, and verification with varying key sizes and compared the results against theoretical expectations. Our findings demonstrate that the system performs as predicted by cryptographic theory, with only minor deviations in computational time complexities. The analysis also reveals significant trade-offs between security and efficiency, particularly when larger key sizes are used. These results confirm that the CONIKS system offers a robust framework for secure and efficient key management, highlighting its potential for real-world applications in secure communication systems.
Open access
Cryptography and Data Security
Security in Wireless Sensor Networks
Advanced Steganography and Watermarking Techniques
This paper proposes a new blockchain-based transaction verification infrastructure for co-payment and data verification for multi-modal public transportation systems.Our solution offers a decentralized platform that ensures secure copayments and data integrity while addressing interoperability, data security and transactional transparency.With a private blockchain, transportation providers act as nodes and validated, consensus-approved transactions increase trust and transparency.A standardized data format and robust algorithms for data contribution by transport operators are developed as well as a model for operators, assets, and transactions.Including zero-knowledge proofs improves user privacy by allowing secure authentication without revealing sensitive data.We believe that this research may lead a closer collaboration between public transport operators and provide an enhanced user experience while enabling transport transaction security and data verification.
Blockchain technology has transformed digital transactions by enhancing security and trust through decentralization and cryptography. However, this technology also poses significant privacy challenges in transactions. Specifically, the transparency inherent in blockchain can lead to privacy concerns where sensitive transaction data and user identities are potentially exposed. This paper explores the dichotomy of data privacy and identity privacy within blockchain transactions, using Ethereum as a primary example. It delves into various privacy protection technologies currently in use, such as homomorphic encryption and zero-knowledge proofs, and evaluates their effectiveness and limitations. The paper further discusses the balance between privacy protection and regulatory compliance, which remains a critical challenge. Additionally, it considers future directions for enhancing privacy without compromising the security and functionality of blockchain systems. This exploration is vital for developing blockchain applications that maintain user confidentiality while benefiting from the transparency and security that blockchain provides.
Privacy computing involves the extensive exchange and processing of encrypted data. For the parties involved in these interactions, how to determine the consistency of exchanged data without accessing the original data, ensuring tamper resistance, non-repudiation, quality traceability, indexing, and retrieval during the use of encrypted data, which is a key topic of achieving "Data Availability versus Visibility". This paper proposes a new type of homomorphism: Feature Homomorphism, and based on this feature, introduces a cryptographic scheme for data verification under ciphertext-only conditions. The proposed scheme involves designing a group of algorithms that meet the requirements outlined in this paper, including encryption/decryption algorithms and Feature Homomorphic Algorithm. This group of algorithms not only allows for the encryption and decryption of data but also ensures that the plaintext and its corresponding ciphertext, encrypted using the specified encryption algorithm, satisfy the following property: the eigenvalue of the plaintext obtained using the Feature Homomorphic Algorithm is equal to the eigenvalue of the ciphertext obtained using the same algorithm. With this group of algorithms, it is possible to verify data consistency directly by comparing the eigenvalues of the plaintext and ciphertext without accessing the original data (i.e., under ciphertext-only conditions). This can be used for tamper resistance, non-repudiation, and quality traceability. Additionally, the eigenvalue can serve as a ciphertext index, enabling searchable encryption. This scheme completes a piece of the puzzle in homomorphic encryption. Keywords: Privacy Computing, Data Consistency, Searchable Encryption, Zero-Knowledge Proof, Feature Homomorphism
Michael Olayinka Gbadebo, Ademola Oluwaseun Salako, Oluwatosin Selesi-Aina, Olumide Samuel Ogungbemi · 6 authors
This study examines the effectiveness of current data privacy protocols within cryptocurrency platforms, focusing on encryption strength, anonymity techniques, and AI-powered regulatory compliance tools. Data were sourced from CoinMarketCap and Kaggle, including metrics like Bit Strength, Breach Incidents, and Anonymity Scores, which were analyzed using descriptive statistics, t-tests, and logistic regression. Results showed no significant relationship between encryption strength and breach incidents (p = 0.817), indicating that encryption strength may not be a primary factor in breach prevention. The weak correlation between encryption strength and breaches suggests that other elements, such as platform vulnerabilities or user behaviour, could play a more critical role in security. AI systems, evaluated through metrics like precision (0.168), recall (0.204), and F1 score (0.184), struggled with false positives, showing limitations in accurately detecting breaches and highlighting the need for more refined AI models. Advanced blockchain technologies like Zero-Knowledge Proofs and Homomorphic Encryption enhanced privacy but increased computational costs. It is recommended that hybrid encryption methods be adopted to balance privacy and performance and improve AI systems for more accurate breach detection. Governments must create clear regulations that encourage innovation while ensuring compliance.
In the context of the digital age, data privacy and security issues are increasingly prominent. Blockchain technology plays an important role in data sharing due to its transparency and immutability, but it also brings the risk of privacy leakage. Zero knowledge proof technology provides a solution for verifying data correctness without exposing data content, which is particularly important for blockchain as it can ensure the validity and compliance of transactions while protecting user privacy. Although zero knowledge proof is quite mature in theory, its application in blockchain systems still faces challenges such as computational efficiency, complexity of smart contracts, and system compatibility. This study aims to propose a privacy protection scheme that supports interactive zero knowledge proof by improving the homomorphic encryption Paillier algorithm, in order to enhance the privacy protection capability of blockchain systems and maintain system efficiency and security. The study will adopt an interdisciplinary approach, combining cryptography, computer science, and network security theory, to deeply analyze the application effect of zero knowledge proof technology in blockchain, explore its optimization space and applicability.
The rapid increase in the number of electric vehicles (EVs) has resulted in huge fuel tax losses for governments every year. Many countries have levied taxes based on the annual or monthly travel record (TR) submitted by the EV. On the one hand, TR contains important private information, such as the time, locations, and trajectories of EV owners. On the other hand, EV owners may forge TR to reduce taxes. Therefore, the verification protocol of TR requires extremely high security and effectiveness. To solve this outstanding issue, this paper proposes a V2I-SNARK protocol that combines vehicle-to-infrastructure communications (V2I) and zk-SNARK for TR verification of EVs. V2I -SNARK is divided into two stages, the trusted setup stage and the TR verification stage. In the former stage, a trusted authority (TA) will generate the proof key and verification key for verification and store them on the verification server (Verifier). In the latter stage, EV will use the proof key to generate a randomized proof, and the verifier will use the verification key to verify the proof. Regarding the performance of the V2I -SNARK protocol, we first provide security proofs for completeness, soundness, and zero-knowledge properties. Furthermore, we compare the verification efficiency, energy consumption, computational complexity, and other performance of V2I-SNARK with the benchmark protocols. The results show that the proposed V2I-SNARK protocol outperforms other protocols in terms of verification efficiency and energy consumption.
Martin Farkas, Balaźs Ádám Toldi, Bertalan Zoltán Péter, Imre Kocsis
In most domains where declarative policies are employed, it is typically the executor of the policy who performs policy evaluation, and not the subjects of policies. However, this approach has evident drawbacks from the trust, transparency and privacy aspects, especially when the subjects are natural persons. Building on recent developments in noninteractive zero-knowledge proofs and the technologies and standards supporting Self-Sovereign Indentity solutions, in this paper, we propose Self-Evaluated Policies, which move policy evaluation to the subject and leave the executor in a (zero-knowledge) proof-checking role. We present an SSI-based system model, propose a proof-tree-checking computational model for zero-knowledge proofs over the evaluations of Prolog-based policies, and describe a Circombased prototype.
A. Jabbari, Gowri Ramachandran, Sidra Malik, Raja Jurdak
In the current digital landscape, supply chains have transformed into complex networks driven by the Internet of Things (IoT), necessitating enhanced data sharing and processing capabilities to ensure traceability and transparency. Leveraging Blockchain technology in IoT applications advances reliability and transparency in near-real-time insight extraction processes. However, it raises significant concerns regarding data privacy. Existing privacy-preserving approaches often rely on Smart Contracts for automation and Zero Knowledge Proofs (ZKP) for privacy. However, apart from being inflexible in adopting system changes while effectively protecting data confidentiality, these approaches introduce significant computational expenses and overheads that make them impractical for dynamic supply chain environments. To address these challenges, we propose ZK-DPPS, a framework that ensures zero-knowledge communications without the need for traditional ZKPs. In ZK-DPPS, privacy is preserved through a combination of Fully Homomorphic Encryption (FHE) for computations and Secure Multi-Party Computations (SMPC) for key reconstruction. To ensure that the raw data remains private throughout the entire process, we use FHE to execute computations directly on encrypted data. The "zero-knowledge" aspect of ZK-DPPS refers to the system's ability to process and share data insights without exposing sensitive information, thus offering a practical and efficient alternative to ZKP-based methods. We demonstrate the efficacy of ZK-DPPS through a simulated supply chain scenario, showcasing its ability to tackle the dual challenges of privacy preservation and computational trust in decentralised environments.
Enterprise resource planning (ERP) platforms integrate critical business operations, including manufacturing, inventory, accounting, human resources, and supply chain management, into unified systems for automation and analytics. However, traditional centralized ERP architectures have notable limitations around security, transparency, costs, and process integrity. ERP data silos inhibit trust, provenance tracking, and collaboration across organizational boundaries. Recently, blockchain distributed ledger technology has emerged as a promising approach to transforming ERP by enabling decentralized verifiable workflows, immutable record keeping, and end-to-end transaction traceability. Blockchain offers a paradigm shift for ERP by distributing control across a peer-to-peer network organized around consensus, cryptography, and innovative algorithms. Early research and prototypes demonstrate blockchain's potential to enhance security, trust, provenance, automation, and standardization in enterprise systems. Blockchain ERP pilots show feasibility in supply chain tracking, accounting, procurement, manufacturing, and more. By sharing tamper-evident ledgers across companies, blockchain builds transparency, integrity, and collaboration into ERP workflows. Several technical concepts underpin blockchain ERP capabilities. Distributed ledgers cryptographically chain transaction records. Consensus protocols like proof-of-work and Byzantine fault tolerance enable unanimous agreement on valid state changes across nodes. Smart contracts automate multi-step workflows based on predefined conditions. Hashing, public-key encryption, and zero-knowledge proofs provide security and privacy. Together, these constructs allow decentralized and verifiable ERP processes. However, challenges remain prior to enterprise adoption at scale. Blockchain ERP must still overcome hurdles integrating with legacy systems, coordinating complex cross-organizational ecosystems, scaling transaction throughput and data storage, and reducing implementation costs. Data privacy also requires consideration under public blockchain models. In conclusion, blockchain shows immense yet nascent promise for revolutionizing outdated ERP platforms once integration, coordination, scaling, and cost obstacles are surmounted through further research and development. By transforming how enterprises architect and optimize mission-critical systems, blockchain may profoundly disrupt enterprise computing and business processes. The technology remains in its early stages but holds revolutionary potential for ERP and inter-organizational collaboration.
Redmond R. Shamshiri, Abdullah Kaviani Rad, Maryam Behjati, Siva K. Balasundram
The challenges and drawbacks of manual weeding and herbicide usage, such as inefficiency, high costs, time-consuming tasks, and environmental pollution, have led to a shift in the agricultural industry toward digital agriculture. The utilization of advanced robotic technologies in the process of weeding serves as prominent and symbolic proof of innovations under the umbrella of digital agriculture. Typically, robotic weeding consists of three primary phases: sensing, thinking, and acting. Among these stages, sensing has considerable significance, which has resulted in the development of sophisticated sensing technology. The present study specifically examines a variety of image-based sensing systems, such as RGB, NIR, spectral, and thermal cameras. Furthermore, it discusses non-imaging systems, including lasers, seed mapping, LIDAR, ToF, and ultrasonic systems. Regarding the benefits, we can highlight the reduced expenses and zero water and soil pollution. As for the obstacles, we can point out the significant initial investment, limited precision, unfavorable environmental circumstances, as well as the scarcity of professionals and subject knowledge. This study intends to address the advantages and challenges associated with each of these sensing technologies. Moreover, the technical remarks and solutions explored in this investigation provide a straightforward framework for future studies by both scholars and administrators in the context of robotic weeding.
This review examines how blockchain technology can be leveraged to enhance data privacy and security in sustainable supply chain management (SSCM). As global supply chains become increasingly complex and the demand for sustainability grows, ensuring data privacy and security has become a critical concern. Traditional supply chain systems often face challenges such as data breaches, lack of transparency, and difficulty in tracing products and materials. Blockchain technology, with its decentralized, immutable, and transparent architecture, offers a promising solution to these challenges. Blockchain can enhance data security by ensuring that data is tamper-proof, traceable, and encrypted, thus protecting sensitive information across the supply chain. It provides transparency while allowing permissioned access, ensuring that stakeholders can verify data without exposing confidential information. Furthermore, privacy-preserving technologies such as zero-knowledge proofs and homomorphic encryption allow verification of data without compromising its security. Smart contracts enable automated compliance with regulatory frameworks like GDPR, reducing the risk of human error and improving operational efficiency. The integration of blockchain in SSCM can improve traceability, transparency, and accountability, thereby promoting environmental and social sustainability. By tracking the origin and journey of goods, blockchain helps verify ethical sourcing practices and reduce carbon footprints. However, the technology also presents challenges, including scalability, integration with legacy systems, and cost considerations. Through case studies in industries such as food, textiles, and renewable energy, this review highlights the practical applications and benefits of blockchain for SSCM. It concludes that blockchain has the potential to revolutionize supply chain operations, but careful consideration must be given to overcoming its technical and financial barriers to widespread adoption.
In blockchain-based data trading platforms, the illegal resale of image data has led to serious copyright infringement issues. Traditional protective measures typically rely on embedding digital watermarks into images to verify data ownership and integrity. However, to ensure the correctness of the watermark embedding process, the embedded results need to be verified. Existing zero-knowledge proof schemes like Groth16 offer efficient verification but are limited in practical applications due to the high computational costs associated with the trusted setup phase required for third-party computations. To address this issue, this paper proposes a watermark embedding and verification mechanism based on block-based processing. By partitioning the image and embedding the watermark incrementally, the time required for the trusted setup and zkey generation in zero-knowledge proofs is significantly reduced, thereby improving the overall efficiency of proof generation. Experimental results show that after partitioning, the total proof generation time is reduced to 24.274% of that in the unpartitioned case. This demonstrates that the proposed method significantly reduces proof generation time, making it more efficient for large-scale image data transactions in blockchain environments.
Advanced Steganography and Watermarking Techniques
Zero-knowledge virtual machines (zkVMs) enable verifiable computation on via succinct Zero-knowledge proofs (ZKPs). However, current zkVMs, still in development, show many bugs. This paper introduces a parameterized framework for the formal verification of zkVMs in Coq. We prove the soundness and completeness of the constraint generation algorithm from machine instructions to semantics-level constraints. Existing works target specific zkVMs, and require repeated proof work in this phase, whereas our proofs are parameterized and can be reused in development and by all zkVMs. We also demonstrate the generality of our framework by instantiation on two examples: Cairo VM and a simplified zkEVM.
The research paper explores the concept of selective disclosure within blockchain networks, addressing the tension between transparency and privacy. It introduces cryptographic schemes that allow users to reveal only necessary information, maintaining confidentiality. The paper analyzes various selective disclosure methods, such as Zero-Knowledge Proofs and Attribute-Based Encryption, highlighting their applications in enhancing privacy and security in digital transactions. It also discusses the implementation of these schemes in blockchain for certification system, emphasizing their potential to foster trust and promote wider adoption of blockchain technology across different industries. The paper concludes by identifying future research directions to optimize these cryptographic protocols for better scalability and interoperability in blockchain systems.
Blockchain Technology Applications and Security
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques