Current crime reporting systems suffer from data distortion, lack of transparency, and privacy vulnerabilities, undermining their credibility. This paper proposes a blockchainbased DApp for secure and transparent crime reporting, integrating advanced encryption, zero-knowledge proofs, instant notifications, and a role-based access mechanism to ensure integrity, anonymity, and clarity. Rigorous testing will demonstrate its functionality, reliability, and user satisfaction, offering improved security, privacy, and transparency over traditional methods.
Blockchain technology, with its decentralization as well as tamper-proof characteristics, has achieved wide application in major fields in recent years. However, because of the potential of privacy leakage that comes with its transparency, privacy protection technology has emerged as a key area of current blockchain research. The first step involves reviewing the blockchain's architecture and selecting a summary of the privacy threats posed by the four layers of the blockchain: data, network, transaction, and application. Next, it concentrates on describing the two more significant types of blockchain privacy protection technology: zero-knowledge proof and homomorphic encryption. The former is developed from its fundamental ideas, application scenarios in the blockchain, and performance and security analysis. Conversely, zero-knowledge proof is derived from three from three aspects of its basic concept, application in blockchain, and technical challenges; finally, the privacy protection technology in blockchain is summarized and a prediction of its future research direction development is made.
Currently, PAKE (Password Authenticated Key Exchange) protocols on lattice using a single-server architecture are widely applied. However, such protocols are vulnerable to server leakage attacks, dictionary attacks, and other threats. To address these issues, researchers have proposed multi-server and two-server architecture-based PAKE protocols. However, PAKE protocols in a multi-server architecture require the use of complex cryptographic primitives such as signatures, and zero-knowledge proofs to ensure security, which reduces the execution efficiency of the protocol. To tackle these challenges, we propose two new multi-server password authentication key exchange protocols based on the MLWE (Module learning with errors) problem. Both protocols rely on MLWE instances, using Peikert's error coordination technique to enable two parties with similar values to compute the same result. Furthermore, we introduce the error pairing assumption and proves its security within random oracle model. The proposed protocol divides the password information into different shares and stores them on separate servers. In protocol 1, all servers and user collaboratively generate session keys, making it suitable for high-security application scenarios. In protocol 2, both user and servers generate session keys individually, which is ideal for high-efficiency application scenarios. Compared to similar protocols, both protocols lower computation and communication costs, better addressing practical application needs while providing protection against quantum computing attacks and server leakage threats.
With the continuous development of blockchain technology, an increasing number of scholars have begun to consider the harm of data leakage during on-chain transactions and the requirement for privacy data protection. Zero-knowledge range proof, as a cryptographic technology, can perform legitimacy verification of data while hiding private data, effectively realizing the protection of private data on the blockchain, so it is increasingly used to protect blockchain privacy. The mainstream construction methods for range proofs can be mainly divided into two categories: n-ary decomposition and square decomposition. This paper introduces and analyzes the advantages and disadvantages of these construction methods in detail. Then, based on these two methods, a zero-knowledge range proof scheme based on multibit split square decomposition (ZKRPMSSD) is proposed, which requires no trusted third-party setting and can achieve range proofs for arbitrary ranges. The proposed ZKRPMSSD scheme processes the original data based on the multibit split idea, and the acquisition method of secret value components is optimized so that the acquisition of components does not depend on the scale of the original problem. Additionally, the algorithms for proof generation and verification in the ZKRPMSSD scheme are redesigned based on the \(\Sigma\) protocol and Pedersen commitments, effectively reducing the computational cost of the proof generation and verification process. Finally, typical n-ary decomposition and square decomposition zero-knowledge range proof construction schemes are taken for comparative analysis. Under 256-bit security and the same problem scale, experimental results indicate that ZKRPMSSD has advantages in proof and verification time costs.
Open access
Cryptography and Data Security
Digital Filter Design and Implementation
Advanced Steganography and Watermarking Techniques
Phuc-Hung Pham Le, Trung-Tin Tran, Toan Q. Dinh, Quy N.
As end-to-end encryption (E2EE) becomes the standard for secure communication, ensuring message authenticity while maintaining user privacy poses significant challenges.This paper introduces the BL0K-ME protocol, a novel cryptographic solution that combines Zero-Knowledge Proofs (ZKP), RSA encryption, and Bloom filters to authenticate individual messages within E2EE conversations.RSA encryption is employed to secure the transmission of messages between users, ensuring that only the intended recipient can decrypt the content, while ZKP enables third-party verification of specific message content without exposing the entire conversation.By leveraging Bloom filters, the protocol provides efficient logging and verification of message existence, balancing privacy protection with legal and regulatory requirements for digital evidence.BL0K-ME addresses a critical gap in current messaging systems by allowing service providers to verify message authenticity for legal investigations without compromising the confidentiality of unrelated communications.This research demonstrates the potential of integrating RSA encryption, ZKP, and Bloom filters to offer a scalable, secure solution for message authentication in E2EE systems, safeguarding both user privacy and the integrity of digital evidence.
In view of the problems of false property rights and difficulties in identity authentication in intellectual property transactions, an identity authentication model for intellectual property transactions based on an alliance chain is proposed. Firstly, the two-factor identity authentication model's roles, functions, and processes are constructed. Secondly, the two-factor authentication mechanism of ID password combined with physiological and property rights features is proposed, the identity identification generation method of fingerprint biometrics and intellectual property features is established, and the constraint compression strategy based on Poseidon hash is designed to reduce the workload of zero-knowledge proof algorithm and realize the consistency of property rights identity. Finally, the security and performance analysis of the authentication model is carried out, and the comparison and validation of related models are carried out, which shows that the model has good security and reliability.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Multi-scalar multiplication (MSM) is crucial in cryptographic applications and computationally intensive in zero-knowledge proofs. MSM involves accumulating the products of scalars and points on an elliptic curve over a 377-bit modulus, and the Pippenger algorithm converts MSM into a series of elliptic curve point additions (PADDs) with high parallelism. This study investigates accelerating MSM on the Versal ACAP platform, an emerging hardware that employs a spatial architecture integrating 400 AI Engines (AIEs) with programmable logic and a processing system. AIEs are SIMD-based VLIW processors capable of performing vector multiply-accumulate operations, making them well-suited for multiplication-heavy workloads in PADD. Unlike simpler multiplication tasks in previous studies, cryptographic computations also require complex operations such as carry propagation. These operations necessitate architecture-aware optimizations, including intra-core dedicated coding style to fully exploit VLIW capabilities and inter-core strategy for spatial task mapping. We propose various optimizations to accelerate PADDs, including (1) algorithmic optimizations for carry propagation employing a carry-save-like technique to exploit VLIW and SIMD capabilities and (2) a comparison of four distinct spatial mappings to enhance intra- and inter-task parallelism. Our approach achieves a computational efficiency that utilizes 50.2% of the theoretical memory bandwidth and provides 568 speedup over the integrated CPU on the AIE evaluation board.
Ensuring scalability in cryptocurrency systems is significant in guaranteeing real-world utility along with the remarkable increment of cryptographic currency. As an alternative in solving scalability issue, payment channel allows users to deliver extensive offline transactions without uploading massive transaction details to the blockchain, such that increasing efficiency can be achieved. However, the implementation of payment channel still encounters privacy concerns when considering the publicly available transaction amounts and the potentials in mining associations between transaction parties. In this paper, we propose a novel payment channel scheme, entitledCommitment-basedAnonymousPayment ChannEl (CAPE), to facilitate unlimited off-chain bidirectional payments while guaranteeing participants’ privacy. The proposed scheme adopts zero-knowledge proof (zk-SNARKs) and verifiable timed (VTD) commitments to ensure the anonymity of the relationship between on-chain and off-chain transactions, privacy of transaction amounts, and security of balances. We comprehensively formalize security definitions and present rigorous proofs for each security attribute. Experiment results further demonstrate the practical viability of CAPE.
The Internet of Behaviors (IoB) is an emerging concept that utilizes devices to collect human behavior and provide intelligent services. Although some research has focused on human behavior analysis and data collection within IoB, the associated security and privacy challenges remain insufficiently explored. This article analyzes the security and privacy risks at different stages of behavioral data generating, uploading, and use while also considering the dynamic characteristics of user activity areas. Then, we propose a blockchain-based distributed IoB data storage and sharing framework, which is categorized into sensing, processing, and management layers based on these stages. To accommodate both identity authentication and behavioral privacy, zero-knowledge proofs are used in the sensing layer to separate the correlation between behavior and identity, which is further extended to a distributed architecture for cross-domain authentication. In the processing layer, an improved consensus protocol is proposed to enhance the decision-making efficiency of distributed IoB by analyzing the geographical and computational capability of the servers. In the management layer, user permission differences and the privacy of access targets are considered. Different types of behavior are modeled as corresponding relationships between keys, and fine-grained secure access is achieved through function secret sharing. Simulation results demonstrate the effectiveness of the proposed framework in multi-scenario IoB, with average consensus and authentication times reduced by 74 percent and 56 percent, respectively.
The syndrome decoding problem is one of the NP-complete problems lying at the foundation of code-based cryptography. The variant thereof where the distance between vectors is measured with respect to the Lee metric, rather than the more commonly used Hamming metric, has been analyzed recently in several works due to its potential relevance for building more efficient code-based cryptosystems. The purpose of this article is to present a zero-knowledge proof of knowledge for this variant of the problem.
This article examines the integration of blockchain technology with enterprise resource planning (ERP) systems to enhance supply chain transparency and data integrity. Blockchain technology creates immutable, decentralized digital ledgers that fundamentally transform how organizations track transactions, verify product authenticity and maintain regulatory compliance. The implementation architecture features multi-layered components, including consensus mechanisms, smart contracts, middleware integration, and application interfaces that connect seamlessly with existing ERP environments. Through end-to-end traceability, cryptographic verification, and fraud prevention mechanisms, blockchain addresses critical vulnerabilities in traditional supply chains across pharmaceuticals, food production, automotive, and luxury goods sectors. Smart contracts further automate complex business processes, particularly in procurement cycles, by converting traditional agreements into self-executing code. Despite significant benefits, organizations face integration complexities, scalability constraints, and change management challenges that must be addressed for successful adoption. Future developments include cross-chain interoperability, zero-knowledge proofs, artificial intelligence integration, and asset tokenization that will further enhance enterprise blockchain capabilities.
Anna Maria Fink, Johanna Nattenmüller, Stephan Rau, Alexander Rau · 10 authors
OBJECTIVES: This study evaluated the effect of enhancing a GPT-4 model with retrieval-augmented generation on its ability to diagnose and classify traumatic injuries based on radiology reports. MATERIALS AND METHODS: In this prospective proof-of-concept study, we used retrieval-augmented generation as a zero-shot learning approach to provide expert knowledge from the RadioGraphics top ten reading list for trauma radiology to the GPT-4 model, creating the context-aware TraumaCB. Radiological report findings of 50 traumatic injuries were independently generated by two radiologists. The performance of the TraumaCB compared to the generic GPT-4 was evaluated by three board-certified radiologists, assessing the accuracy and trustworthiness of the chatbot responses in the 100 reports created. RESULTS: The TraumaCB achieved 100% correct diagnoses, 96% correct classification, and 87% correct grading, outperforming the generic GPT-4 with 93% correct diagnoses, 70% correct classification, and 48% correct grading. TraumaCB sources consistently achieved a median rating of 5.0 for explanation and trust. Challenges encountered mainly involved traumatic injuries lacking widely accepted classification systems. CONCLUSION: Augmenting a commercial GPT-4 model with retrieval-augmented generation improves its diagnostic and classification capabilities, positioning it as a valuable tool for efficiently assessing traumatic injuries across various anatomical regions in trauma radiology. KEY POINTS: Question Retrieval-augmented generation has the potential to enhance generic chatbots with task-specific knowledge of emergency radiology. Findings The TraumaCB excelled in accuracy, particularly in injury classification and grading, and provided explanations along with the sources used, increasing transparency and facilitating verification. Clinical relevance The TraumaCB provides accurate, fast, and transparent access to trauma radiology classifications, potentially increasing the efficiency of image interpretation in emergency departments and enabling customized reports based on local or individual preferences.
Open access
Artificial Intelligence in Healthcare and Education
The advent of quantum computing poses an existential threat to contemporary cryptographic standards, particularly those securing decentralized blockchain networks and cloud infrastructures.Classical public-key cryptosystems such as RSA, ECC, and DH, which rely on factorization and discrete logarithm problems, are rendered obsolete by Shor's algorithm, necessitating the transition toward post-quantum cryptographic (PQC) solutions.This study explores the integration of PQC algorithms, including lattice-based, hash-based, code-based, multivariate, and isogeny-based cryptographic mechanisms, within blockchain-ledger technologies and cloud architectures to ensure long-term security against quantum adversaries.A comparative analysis is conducted to evaluate computational efficiency, key size implications, communication overhead, and security resilience under quantum attack models.The research highlights the adaptation of PQC within blockchain consensus mechanisms, smart contract execution, and cryptographic primitives such as digital signatures, zero-knowledge proofs, and secure multi-party computation (MPC).Additionally, it examines the impact of PQC on cloud security, addressing challenges in quantum-safe key exchange protocols, homomorphic encryption for secure computations, and cross-platform interoperability within hybrid quantum-classical cloud ecosystems.Real-world implementations and benchmarking data provide insights into the feasibility of large-scale adoption, shedding light on standardization efforts by NIST and industry consortia.The study concludes with future directions, emphasizing the need for efficient PQC algorithm optimization, lightweight cryptographic frameworks for IoT-driven blockchain applications, and scalable post-quantum identity management systems.By establishing quantum-resistant security frameworks, this research underscores the imperative need for early adoption to mitigate cryptographic vulnerabilities in the impending post-quantum era.
Anamika Nath, Souhardyo Bhattacharjee, Muhammad Ibrahim Khan
Identity management systems play a crucial role in ensuring security, privacy, and efficiency in various online transactions and interactions. Traditional identity management systems are often centralized, posing significant risks such as single points of failure, data breaches, and privacy concerns. Decentralized identity management systems leveraging blockchain technology and smart contracts offer a promising solution to address these challenges. The system aims to provide a secure, efficient, and privacy-preserving solution for managing digital identities. Through the utilization of blockchain technology, Smart Identity offers immutable and tamper-resistant records of identities, enhancing trust and transparency in digital interactions. In this proposed system, we have used a zero-knowledge-proof mechanism to get the IPFS link from the authenticated documents. These authenticated documents include a national identity card, birth certificate, passport copy, and image, all of which are unique to individual users. Consequently, it has ensured that no one can generate a valid IPFS link more than once. Upon completing the form in the system with a valid IPFS link, the information has been securely stored in the blockchain, and a unique identity has been generated. This identity helps the user with multipurpose work. In this system, we have used Sepolia Testnet for real-time transactions. We have created a blockchain network using the Metamask wallet to deploy our smart contracts. Furthermore, we have designed a model that can create a unique identity and also modify information without violating the rules of blockchain technology. We have also proposed a unique identity creation model using a real-life IPFS link generator system that is also created by our system. Overall, Smart Identity represents a significant step toward the realization of a more secure, user-centric, and decentralized approach to identity management in the digital realm.
Jihye Choi, Sai Rahul Rachuri, Ke Wang, Somesh Jha · 5 authors
Federated Learning (FL) enables collaborative model training while keeping client data private. However, exposing individual client updates makes FL vulnerable to reconstruction attacks. Secure aggregation mitigates such privacy risks but prevents the server from verifying the validity of each client update, creating a privacy-robustness tradeoff. Recent efforts attempt to address this tradeoff by enforcing checks on client updates using zero-knowledge proofs, but they support limited predicates and often depend on public validation data. We propose SLVR, a general framework that securely leverages clients' private data through secure multi-party computation. By utilizing clients' data, SLVR not only eliminates the need for public validation data, but also enables a wider range of checks for robustness, including cross-client accuracy validation. It also adapts naturally to distribution shifts in client data as it can securely refresh its validation data up-to-date. Our empirical evaluations show that SLVR improves robustness against model poisoning attacks, particularly outperforming existing methods by up to 50% under adaptive attacks. Additionally, SLVR demonstrates effective adaptability and stable convergence under various distribution shift scenarios.
The growing power of large language models (LLMs) has revolutionized how people access and utilize information. Notably, the LLMs excel at performing fine-grained data representation, which facilitates precise retrieval of information. They also generate high-quality answers based on external references, enabling the production of useful knowledge. The recent introduction of reasoning models, like OpenAI O1 and DeepSeek R1, marks another leap forward, highlighting LLMs' ability to think progressively before delivering final answers. This breakthrough significantly improves the ability to address complex tasks, e.g., coding and math proofs. Inspired by this progress, we aim to develop similar capabilities for retrieval models, which hold great promise for tackling critical challenges in the field, including multi-task retrieval, zero-shot retrieval, and tasks requiring intensive reasoning of complex relationships. With this motivation, we propose a novel approach called O1 Embedder, which generates useful thoughts for the input query before making retrieval for the target documents. To realize this objective, we conquer two technical difficulties. First, we design a data synthesis workflow, creating training signals for O1 Embedder by generating initial thoughts from an LLM-expert and subsequently refining them using a retrieval committee. Second, we optimize the training process, enabling a pre-trained model to be jointly fine-tuned to generate retrieval thoughts via behavior cloning and perform dense retrieval through contrastive learning. Our approach is evaluated by comprehensive experiments, where substantial improvements are achieved across 12 popular datasets, spanning both in-domain and out-of-domain scenarios. These results highlight O1 Embedder's remarkable accuracy and generalizability, paving the way for the development of next-generation IR foundation models.
Blockchain stores and writes all the transactions because of the unlimited storage capacity. Leader election is the process of electing a node as an overall incharge of the distributed network. Leader election is a complicated task as we have to choose a leader by giving equal opportunity to all the nodes. We implement all the algorithms of the DONS protocol in order to elect a leader but in our TBNS (Trust Based Neighbor Selection) protocol, we add zk-SNARKs proof to enhance the security of Blockchain. zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) is a type of proof used in cryptography to prove the authenticity of information without revealing any additional information. It allows one party to prove to another that they know a certain piece of information without actually revealing the information itself. In the end, the results of our proposed model are compared with RTT-NS and DONS.
Traditional banking systems rely heavily on centralized data storage and identity verification processes, leading to security vulnerabilities and regulatory burdens. Zero-Knowledge Proof (ZKP) technologies offer a cryptographic solution that enables financial verification-such as identity authentication, balance confirmation, and transaction validation-without disclosing sensitive user data. This paper explores the integration of zk-SNARK and zk-STARK protocols, ZK-Rollup-based scalability solutions, and zkLedger applications within banking operations. It examines the role of selective disclosure mechanisms in critical areas such as KYC/AML compliance, balance verification, cross-border transactions, and regulatory audits. Furthermore, a four-layer architectural model is proposed to facilitate the seamless integration of ZKP-based frameworks into traditional banking infrastructures, addressing key aspects such as security optimizations, transaction costs, and scalability. Lastly, the potential of ZKP in regulatory compliance, its bridging role between DeFi and centralized banking, and the emergence of ZKP-driven next-generation banking services are discussed.
Distributed certification is a set of mechanisms that allows an all-knowing prover to convince the units of a communication network that the network's state has some desired property, such as being 3-colorable or triangle-free. Classical mechanisms, such as proof labeling schemes (PLS), consist of a message from the prover to each unit, followed by one round of communication between each unit and its neighbors. Later works consider extensions, called distributed interactive proofs, where the prover and the units can have multiple rounds of communication before the communication among the units. Recently, Bick, Kol, and Oshman (SODA '22) defined a zero-knowledge version of distributed interactive proofs, where the prover convinces the units of the network's state without revealing any other information about the network's state or structure. In their work, they propose different variants of this model and show that many graph properties of interest can be certified with them. In this work, we define and study distributed non-interactive zero-knowledge proofs (dNIZK); these can be seen as a non-interactive version of the aforementioned model, and also as a zero-knowledge version of PLS. We prove the following: - There exists a dNIZK protocol for 3-coloring with O(log n)-bit messages from the prover and O(log n)-size messages among neighbors. - There exists a family of dNIZK protocols for triangle-freeness, that presents a trade-off between the size of the messages from the prover and the size of the messages among neighbors. - There exists a dNIZK protocol for any graph property in NP in the random oracle models, which is secure against an arbitrary number of malicious parties.
Large language models (LLMs) are increasingly utilized in domains such as finance, healthcare, and interpersonal relationships to provide advice tailored to user traits and contexts. However, this personalization often relies on sensitive data, raising critical privacy concerns and necessitating data minimization. To address these challenges, we propose a framework that integrates zero-knowledge proof (ZKP) technology, specifically zkVM, with LLM-based chatbots. This integration enables privacy-preserving data sharing by verifying user traits without disclosing sensitive information. Our research introduces both an architecture and a prompting strategy for this approach. Through empirical evaluation, we clarify the current constraints and performance limitations of both zkVM and the proposed prompting strategy, thereby demonstrating their practical feasibility in real-world scenarios.
The medical industry has made significant advancements in recent years. However, the lack of accountability in medical management has resulted in systemic deficiencies, which have adversely affected patient trust and contributed to an increase in medical disputes. As a result, there is a growing emphasis on managing the quality of medical services, particularly in enhancing patient experience. To address these challenges, we propose a new system for evaluating health services. This system will allow patients to anonymously rate the services they receive while also providing doctors the opportunity to appeal specific reviews. The hospital handles the evaluations and appeals through the management of the cloud platform. We propose a new scheme to assist the work of the platform, which is a lattice-based group signature with verifier-local revocation (VLR-GS). Most of the work on VLR-GS has focused on the random oracle model (ROM) or using non-interactive zero-knowledge proofs (NIZKs). Our construction is anonymous and traceable in the standard model under the hardness of the learning with errors problem and short integer solution problem. Furthermore, theoretically analyzing it has practical significance in both security and efficiency. In conclusion, the proposed scheme establishes a secure and privacy-oriented platform for an anonymous medical service evaluation system, with the goal of fostering patient trust and improving hospital service quality within the healthcare sector.