A. A. Gerasyov, I. N. Efremova, I. S. Arhipova, R. N. Kamalov
The purpose of research. The actual problem in voting systems is not the prevention of fraud and the failure to ensure the integrity of elections. Therefore, the task of building a system of safe and transparent voting is urgent. The purpose of the research is design and develop an information and computing system based on smart contacts for online voting. Methods. The presented system has the form of a decentralized system that can overcome a number of limitations, such as the likelihood of human error, falsification of voting results and problems with accessibility, using blockchain technology, which provides protection against unauthorized access and transparent record keeping. The use of smart contract technology also ensures that the voting process will be automated, transparent and secure. Thanks to the ability to execute self-executing contracts based on predefined rules, smart contracts can eliminate the need for intermediaries, reduce the likelihood of errors and ensure a high degree of accountability. Results. In order to evaluate the effectiveness of the developed system, testing was carried out, after which a comparative analysis was made with other similar systems. The system can ensure that votes are counted accurately and cannot be manipulated, thereby increasing confidence in the results of the voting process. In addition, the system can potentially reduce the costs associated with traditional voting methods and improve accessibility for voters, since it can be accessed remotely via a web interface. In general, the system will be of interest to various organizations and individuals seeking to conduct safe and transparent voting processes. Conclusion. The use of this software product will provide security and convenience when participating in various online voting, reliable storage of votes and the provision of detailed statistics on the conducted voting.
Open access
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
Daryn Monteiro, Ishaan Mavinkurve, Parth Kambli, Prof. Sakshi Surve
Abstract: Artificial Intelligence has found widespread use across various industries, from optimizing manufacturing workflows to diagnosing health conditions. However, the large volumes of data required to train AI models raise privacy concerns, especially when stored in centralized databases vulnerable to leaks. Federated Learning solves this problem by training models collaboratively by avoiding centralization of the sensitive data, preserving privacy while allowing decentralized models to be exported to edge devices. This paper explores Federated Learning, focusing on its technical aspects, algorithms, and decentralized architecture. By keeping raw data localized, Federated Learning enables global models while safeguarding individual privacy, fostering collaboration across sectors like healthcare, finance, and IoT. It also addresses challenges such as privacy vulnerabilities and model aggregation across devices, proposing solutions to strengthen Federated Learning's effectiveness. Ultimately, this study highlights Federated Learning's pivotal role in the future of AI, where privacy preservation and collaboration are key. By balancing model performance with data privacy, Federated Learning stands as a promising framework for responsible and inclusive AI development.
This paper investigates the use of threshold signature schemes in isogeny-based cryptosystems. By leveraging the distinct features of isogeny graphs, we propose a protocol that offers high security and practical efficiency, making it ideal for distributed ledger technologies and secure multi-party computations. Our scheme is resistant to quantum attacks and maintains minimal computational and communication overheads. We present an extensive analysis of the security and performance of our protocol, emphasizing its relevance to real-world cryptographic systems. MSC 2020: 94A60, 11G07, 68M07.
Attribute-Based Encryption (ABE) is a promising solution for access control in cloud services. However, the heavy decryption overhead hinders its widespread adoption. A general approach to address this issue is to outsource decryption to decryption cloud service(DCS). Existing schemes have utilized various methods to enable users to verify outsourced results; however, they lack an effective mechanism to achieve exemptibility which enables the honest DCS to escape from wrong claims. And it is impractical to assume that the DCS will provide free services. In this paper, we propose a blockchain-based payable outsourced decryption ABE scheme that achieves both verifiability and exemptibility without adding redundant information to ABE ciphertext. We use zero-knowledge proof to verify outsourced results on blockchain and introduce an optional single-round challenge game under optimistic assumption to address the high cost of proof generation. Moreover, our system achieves fairness and decentralized outsourcing to protect the interests of all parties. Finally, we implement and evaluate our scheme on Ethereum to demonstrate its feasibility and efficiency, the gas usage in attribute numbers from 5 to 60 is 11$\times$ to 140$\times$ in the happy case and 4$\times$ to 55$\times$ in the challenge case lower than the scheme of Ge et al. (TDSC'23).
Integrating machine learning (ML) into blockchain consensus mechanisms enhances efficiency, scalability, and resilience. This study introduces the PoA 2 algorithm, an ML-enhanced Proof of Authority mechanism that optimizes signer selection for improved transaction processing. Simulations with models including Random Forest, Logistic Regression, SVM, K-Nearest Neighbors, Decision Tree, and Gradient Boosting showed significant gains. Random Forest reduced latency tenfold, achieving nearly 1000 transactions per second, with 93.33% accuracy, 100% precision, 86.67% recall, and a 92.86% F1-score. These results demonstrate MLâs potential to enhance blockchain performance, making hybrid blockchain-ML solutions a promising research direction.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
With the advancement of cutting-edge technologies, the Internet of Medical Things (IoMT) has assisted the healthcare sector by facilitating interaction between healthcare service providers and patients in remote areas. In IoMT, wearable or implantable sensors collect the patientâs record and share the information through a public network. Health-related information about the patient must be protected from a variety of attacks by the adversary since it is sensitive and extremely vulnerable to attacks. The sensor equipment that is implanted in the patient is also resource-constrained and has a low power capacity. The entities involved in the communication must be authenticated with one another in order to protect patientsâ health information, anonymity, and reliability. While several authenticated key agreement protocols have been proposed, many suffer from high computational costs and storage cost, making them unsuitable for lightweight applications. This paper proposes a secure three-factor robust Elliptic Curve Cryptography (ECC) based mutually authenticated and key agreement protocol known as RELAKA for the IoMT environment, utilizing the benefits of one-way hash function. In proposed scheme, all entities, including the healthcare service providers and wearable sensors, are authenticated by the medical server. Subsequently, a secret key is established for each communication session and shared between all the entities. Additionally, mechanism for appropriate user revocation and re-registration is integrated to provide additional security in cases where a userâs QR code is tampered with by the attacker. The privacy of the proposed protocol is investigated by the potential use of zero knowledge proof. Furthermore, the efficacy of the authentication is examined by challenge and response mechanism. The informal security analysis demonstrates its resistance to threats such as DoS, impersonation, message modification, password guessing, and so on. The performance evaluation of RELAKA protocol indicates that the execution, communication, and storage costs is reduced by 87.59%, 43% and 60.71% respectively. Moreover, the outcomes of the AVISPA simulation illustrate that the RELAKA successfully evades both active and passive attacks. In addition, real-world testbed environment is developed with Raspberry pi 4 model B and the experimental results verifies the robustness of the proposed protocol. According to theoretical analysis and experimental evaluation, the RELAKA scheme is more secure and efficient than the existing protocols.
ABSTRACT With the increasing trend of outsourcing data to cloud services, ensuring data security and privacy has become crucial. Typically, data are stored on cloud servers in encrypted form to mitigate risks. However, accessing the encrypted data requires an access key distributed by a third party. If this third party is untrustworthy, it poses a significant security threat to the system. To address this challenge, we propose a Decentralized Secure Data Outsourcing System (DSDOS) that uses blockchain technology to ensure data security and privacy. The DSDOS system comprises three modules: data security and privacy, access control and authorization, and data integrity and availability. The data security and privacy module uses a hybrid encryption scheme that combines Advanced Encryption Standard (AES), partially homomorphic encryption (PHE), and DiffieâHellman (DH) to ensure secure data storage and access. The access control and authorization module uses a blockchainâbased smart contract system to manage access to the encrypted data. The data integrity and availability module uses hashâbased message authentication code (HMAC) to ensure that the data are not tampered with and is always available. We conducted a security and performance analysis of the DSDOS system and found that it outperforms previous schemes in terms of security and performance. The DSDOS system is a secure and privacyâpreserving data outsourcing system that can be used to mitigate the security risks associated with traditional cloud storage systems.
Francesco DâAmato, Roberto Saltini, Thuy-An Tran, Luca Zanolini
Gasper, the consensus protocol currently employed by Ethereum, typically requires 64 to 95 slots -- the units of time during which a new chain extending the previous one by one block is proposed and voted -- to finalize. This means that under ideal conditions -- where the network is synchronous, and all chain proposers, along with more than two-thirds of the validators, behave as dictated by the protocol -- proposers construct blocks on a non-finalized chain that extends at least 64 blocks. This exposes a significant portion of the blockchain to potential reorganizations during changes in network conditions, such as periods of asynchrony. Specifically, this finalization delay heightens the network's exposure to Maximum Extractable Value (MEV) exploits, which could undermine the network's integrity. Furthermore, the extended finalization period forces users to balance the trade-off between economic security and transaction speed. To address these issues and speed up finality, we introduce a partially synchronous finality gadget, which we combine with two dynamically available consensus protocols -- synchronous protocols that ensure safety and liveness even with fluctuating validator participation levels. This integration results in secure ebb-and-flow protocols [SP 2021], achieving finality within three slots after a proposal and realizing 3-slot finality.
This report presents the results of a PhD thesis focused on the creation of a decentralised and autonomous identity structure. This structure allows users not only to store their identities, but also to generate them autonomously, without the need for a trusted third party or an external validation structure. To implement this concept, the 3BI-ECC framework, a network system based on blockchain and the cryptographic advantages of elliptic curves, has been designed to establish a decentralised consortium network of self-generated identities. In addition, a new blockchain structure called NestedChain was created, which allows two types of information to be stored simultaneously: the identity of users through smart contracts and the certification elements of the network. The Nested-C protocol has also been developed, a new consensus protocol that improves the efficiency and security of storing transactions on the blockchain. In addition, solutions are proposed to preserve the integrity of the information in each smart contract and in its transactions with the theoretical scheme. This research aims to create decentralised environments where each user controls their information on the network, protecting both their identity and the privacy of their data, thus promoting user autonomy in the digital environment.
This paper introduces a novel Distributed Key Generation (DKG) protocol based on the Commutative Supersingular Isogeny Diffie-Hellman (CSIDH) framework for secure multi-party cryptography. Our proposed protocol is designed to address scalability and security concerns, particularly in post-quantum cryptographic systems. The main contributions include the introduction of Piecewise Verifiable Proofs (PVPs) for non-interactive zero-knowledge verification of secret shares, and the provision of rigorous security analysis, including resistance to quantum adversaries via Shorâs and Groverâs algorithms. We analyze the protocolâs efficiency, ensuring low computational overhead even in large-scale systems, and compare it with other distributed cryptographic protocols such as RSA-based and lattice-based schemes. Through mathematical proofs and complexity analysis, we demonstrate that our protocol offers enhanced security, efficiency, and scalability in a post-quantum environment. The results presented in this paper provide a strong foundation for implementing secure multi-party computations in quantum-resistant systems.
Blockchain technology has revolutionized decentralized systems by enabling secure, transparent, and tamper-resistant data management. Central to blockchain functionality is the consensus mechanism, which ensures agreement among distributed nodes. This paper presents a comprehensive review of cryptographic solutions underpinning various blockchain consensus protocols. We analyze the role of cryptographic primitives such as hash functions, digital signatures, zero-knowledge proofs, and threshold cryptography in enhancing security and efficiency of consensus algorithms like Proof of Work (PoW), Proof of Stake (PoS), and Practical Byzantine Fault Tolerance (PBFT). The review highlights strengths, limitations, and emerging trends in cryptographic techniques addressing scalability, privacy, and resistance to adversarial attacks. Finally, we discuss future research directions toward integrating advanced cryptography with blockchain consensus to improve trustworthiness and performance.
Carmit Hazay, Muthuramakrishnan Venkitasubramaniam, Mor Weiss
Abstract Leakage-resilient cryptography aims to protect cryptographic primitives from so-called âside channel attacksâ that exploit their physical implementation to learn their input or secret state. Starting from the works of Ishai, Sahai and Wagner (CRYPTOâ03) and Micali and Reyzin (TCCâ04), most works on leakage-resilient cryptography either focus on protecting general computations, such as circuits or multiparty computation protocols, or on specific non-interactive primitives such as storage, encryption, and signatures. This work focuses on leakage resilience for the middle ground, namely for distributed and interactive cryptographic primitives. Our main technical contribution is designing the first secret sharing scheme that is equivocal , resists adaptive probing of a constant fraction of bits from each share, while incurs only a constant blowup in share size. Equivocation is a strong leakage-resilience guarantee, recently introduced by Hazay et al. (ITC, 2021). Our construction is obtained via a general compiler which we introduce, that transforms any secret sharing scheme into an equivocal scheme against adaptive leakage. An attractive feature of our compiler is that it respects additive reconstruction; namely, if the original scheme has additive reconstruction, then the transformed scheme has linear reconstruction. We extend our compiler to a general paradigm for protecting distributed primitives against leakage and show its applicability to various primitives, including secret sharing, verifiable secret sharing, function secret sharing, distributed encryption and signatures, and distributed zero-knowledge proofs. For each of these primitives, our paradigm transforms any construction of the primitive into a scheme that resists adaptive party corruptions, as well as adaptive probing leakage of a constant fraction of bits in each share when the share is stored in memory (but not when it is used in computations). Moreover, the transformation incurs only a constant blowup in the share size and respects additive reconstructionâan important feature for several of these primitives, such as function secret sharing and distributed encryption.
In the era of digital transformation, securing data on metaverse platforms poses significant challenges. This paper proposes Multiparty Space Sharing and Authentication (MSSA), a novel approach for secure user login and location access control within specialized metaverse platforms. MSSA leverages Quantum Multiparty Secret Computation (QMSC) integrated with a quantum blockchain network. This integration facilitates user verification within the presence of potentially untrusted metaverse authority. The underlying quantum blockchain employs a Delegated Proof-of-Stake (DPoS) consensus mechanism with a Borda voting scheme for authority node selection. By harnessing the principles of quantum cryptography, MSSA offers enhanced security against both classical and anticipated future quantum attacks. This research demonstrates the feasibility and potential of quantum blockchain for securing metaverse platforms, paving the way for secure and decentralized digital ecosystems.
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.
This research paper explores the CONIKS key management systemâs 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
Matteo Loporchio, Anna Bernasconi, Damiano Di Francesco Maesa, Laura Ricci
Decentralized applications, the driving force behind the new Web3 paradigm, require continuous access to blockchain data. Their adoption, however, is hindered by the constantly increasing size of blockchains and the sequential scan nature of their read operations, which introduce a clear inefficiency bottleneck. Also, the growing amount of data recorded on the blockchain makes resource-constrained light nodes dependent on untrusted full nodes for fetching information, with a consequent need for query authentication protocols ensuring result integrity. Motivated by these reasons, in this paper we propose the skip index, an indexing data structure that allows users to quickly retrieve information simultaneously from multiple blocks of a blockchain. Our solution is also designed to be used as an authenticated data structure to guarantee the integrity of query results for light nodes. We discuss the theoretical properties of skip indices, propose efficient algorithms for their construction and querying, and detail their computational complexity. Finally, we assess the effectiveness of our proposal through an experimental evaluation on the Ethereum blockchain. As a reference use case, we focus on the popular CryptoKitties application and simulate a scenario where users seek to retrieve the events generated by the service. Our experimental results suggest that the use of skip indices offers a constant multiplicative speedup, thanks to search times that are at most logarithmic within a chosen search window. This allows to reduce the number of visited blocks by up to two orders of magnitude if compared to the naive sequential approach currently in use. ⢠We propose the skip index, a data structure for efficient blockchain data retrieval. ⢠The skip index provides guarantees about the integrity of query results. ⢠We devise efficient algorithms to construct and query skip indices. ⢠Compared to a sequential scan, skip indices offer a constant multiplicative speedup. ⢠Skip indices experimentally provide a speedup of up to two orders of magnitude.
Open access
Blockchain Technology Applications and Security
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
Proof-of-Authorization (PoA) consensus algorithms are widely used in permissioned blockchain networks due to their high throughput , security, and efficiency. However, PoA is susceptible to cloning attacks, where attackers copy the authenticator identity and key, thereby compromising the consensus integrity. This study proposes a novel randomized authenticator within the PoA framework to mitigate cloning attacks and solve the leader selection bottleneck. The main contributions include 1) Introducing unpredictability in leader selection through Verifiable Random Functions (VRFs) to prevent identity duplication.2) Dynamic group management using a hierarchical decentralized architecture of distributed ledgers that balances security and performance.3) Using threshold signatures to avoid a single point of failure among validators.4) Comprehensively analyzing attacks, security, randomness, and availability.5) Evaluating the effectiveness of a randomized authenticator by means of OMNET++ simulations to assess efficiency. By integrating randomness into leader selection and robust consensus design, the approach enables reliable and secure dynamic group management in decentralized networks.
The world is rapidly heading towards digitization and digital documentation.The COVID-19 pandemic has highlighted the significance of digitization in our daily lives.Nowadays, fake documents are widely available and easy to obtain, harming both our financial system and social trust.Consequently, there is a growing demand for procedures to verify and authenticate various crucial documents, including transactional, financial, governmental, and personal certificates, as well as educational certificates.This type of practice can be done using blockchain and cryptography technology.In this paper, we propose a Blockchain based Digital Identity Management System (BDIMS) that empowers organizations to generate instantaneously authenticated and tamper-resistant digital credentials.It issues a signed document and stores the signature on the blockchain.The verifier can easily verify the signature from the blockchain instantly using the digital signature concept.BDIMS also provides a QR-code system for real-time identity verification.It also introduces zero-knowledge proof for verifying the part of an identity without revealing the original statement.Furthermore, a user can store and share all their identities on a single platform using BDIMS.The proposed model effectively addresses the shortcomings of traditional methods by ensuring a comprehensive and streamlined approach.It successfully bridges the gaps and overcomes the difficulties inherent in conventional document verification systems, meeting all the necessary criteria for a robust and reliable verification process.
This paper addresses privacy protection in decentralized Artificial Intelligence (AI) using Confidential Computing (CC) within the Atoma Network, a decentralized AI platform designed for the Web3 domain. Decentralized AI distributes AI services among multiple entities without centralized oversight, fostering transparency and robustness. However, this structure introduces significant privacy challenges, as sensitive assets such as proprietary models and personal data may be exposed to untrusted participants. Cryptography-based privacy protection techniques such as zero-knowledge machine learning (zkML) suffers prohibitive computational overhead. To address the limitation, we propose leveraging Confidential Computing (CC). Confidential Computing leverages hardware-based Trusted Execution Environments (TEEs) to provide isolation for processing sensitive data, ensuring that both model parameters and user data remain secure, even in decentralized, potentially untrusted environments. While TEEs face a few limitations, we believe they can bridge the privacy gap in decentralized AI. We explore how we can integrate TEEs into Atoma's decentralized framework.
Federated learning, as a novel distributed machine learning mode, enables the training of machine learning models on multiple devices while ensuring data privacy. However, the existence of single-point-of-failure bottlenecks, malicious threats, scalability of federated learning implementation, and lack of incentive mechanisms have seriously hindered the development of federated learning technology. In recent years, as a distributed ledger, blockchain has the characteristics of decentralization, tamper-proof, transparency, security, etc., which can solve the issues encountered in the above-mentioned federated learning. Particularly, the integration of federated learning and blockchain leads to a new paradigm, called blockchain-based federated learning (BFL), which has been successfully applied in many application scenarios. This paper aims to provide a comprehensive review of recent efforts on blockchain-based federated learning. More concretely, we propose and design a taxonomy of blockchain-based federated learning models, along with providing a comprehensive summary of the state of the art. Various applications of federated learning based on blockchain are introduced. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development in the field.
Nikhil Vanjani, Pratik Soni, Sri Aravinda Krishnan Thyagarajan
In scenarios where a seller holds sensitive data $x$, like patient records, and a buyer seeks to obtain an evaluation of a function $f$ on $x$, solutions in trustless environments like blockchain fall into two categories: (1) Smart contract-powered solutions and (2) cryptographic solutions using tools such as adaptor signatures. The former offers atomic transactions where the buyer learns $f(x)$ upon payment. However, this approach is inefficient, costly, lacks privacy for the seller's data, and is incompatible with blockchains such as bitcoin. In contrast, the adaptor signature-based approach addresses all of the above issues but comes with an "all-or-nothing" guarantee, where the buyer fully extracts $x$ and does not support extracting $f(x)$. In this work, we bridge the gap between these approaches, developing a solution that enables fair functional sales while offering all the above properties like adaptor signatures. Towards this, we propose functional adaptor signatures (FAS), a novel cryptographic primitive and show how it can be used to enable functional sales. We formalize the security properties of FAS, among which is a new notion called witness privacy to capture seller's privacy, which ensures the buyer does not learn anything beyond $f(x)$. We present multiple variants of witness privacy, namely, witness hiding, witness indistinguishability, and zero-knowledge. We introduce two efficient constructions of FAS supporting linear functions based on groups of prime-order and lattices, that satisfy the strongest notion of witness privacy. A central conceptual contribution of our work lies in revealing a surprising connection between functional encryption and adaptor signatures. We implement our FAS construction for Schnorr signatures and show that for reasonably sized seller witnesses, all operations are quite efficient even for commodity hardware.
As machine learning spreads into fields of use that demand secure and private authentication, ensuring such authentication is becoming increasingly critical.Zero Knowledge Proofs (ZKPs) have been presented as a cryptographic technique of transforming authentication without data leakage [1].In this research, the use of ZKPs in the AI authentication frameworks is looking into privacy, security and scalability.The model predictions are verified by the proposed system using advanced ZKP protocols like zkSNARKs and zkSTARKs without revealing model parameters or user inputs [3].Our system is able to reach better computational efficiency and lower computation overhead through incorporation of Mystique conversion protocols [7] and fast ZK inference protocols such as ezDPS [6].Results of experiments [5] show that frameworks with ZKP integrated authentication perform better than the standard encryption with respect to both security and performance in decentralized machine learning regimes.Moreover, the solution facilitates verifiability in Federated Learning by integrating blockchain, which helps to increase transparency and trust [4].To overcome the data leakage issue, ZKPs are explored for use in decentralized AI frameworks where secure model deployment is required to generate personalized advice [4].As this research shows, ZKPs offer transformative properties which can be used for authentication in AI systemssuch as in healthcare, finance or IoT network and thus increase the trust in AI driven solutions.
Chenhao Ying, Fuyuan Xia, David S. L. Wei, Xinchun Yu ¡ 11 authors
Harnessing the benefits of blockchain, such as decentralization, immutability, and transparency, to bolster the credibility and security attributes of federated learning (FL) has garnered increasing attention. However, blockchain-enabled FL (BFL) still faces several challenges. The primary and most significant issue arises from its essential but slow validation procedure, which selects high-quality local models by recruiting distributed validators. The second issue stems from its incentive mechanism under the transparent nature of blockchain, increasing the risk of privacy breaches regarding workersâ cost information. The final challenge involves data eavesdropping from shared local models. To address these significant obstacles, this paper proposes a Blockchain-enabled Incentivized and Secure Federated Learning (BIT-FL) framework. BIT-FL leverages a novel loop-based sharded consensus algorithm to accelerate the validation procedure, ensuring the same security as non-sharded consensus protocols. It consistently outputs the correct local model selection when the fraction of adversaries among validators is less than$1/2$with synchronous communication. Furthermore, BIT-FL integrates a randomized incentive procedure, attracting more participants while guaranteeing the privacy of their cost information through meticulous worker selection probability design. Finally, by adding artificial Gaussian noise to local models, it ensures the privacy of trainersâ local models. With the careful design of Gaussian noise, the excess empirical risk of BIT-FL is upper-bounded by$\mathcal {O}(\frac{\ln n_{\min}}{ n_{\min}^{3/2}}+\frac{\ln n}{n})$, where$n$represents the size of the union dataset, and$n_{{\min}}$represents the size of the smallest dataset. Our extensive experiments demonstrate that BIT-FL exhibits efficiency, robustness, and high accuracy for both classification and regression tasks.