Folding schemes are an exciting new primitive, transforming the task of performing multiple zero-knowledge proofs of knowledge for a relation into performing just one zero-knowledge proof, for the same relation, and a number of cheap inclusion-proofs. Recently, folding schemes have been used to amortize the cost associated with proving different statements to multiple distinct verifiers, which has various applications. We observe that for these uses, leaking information about the statements folded together can be problematic, yet this happens with previous constructions. Towards resolving this issue, we give a natural definition of privacy preserving folding schemes, and what security they should offer. To construct privacy preserving folding schemes, we first define statement hiders, a primitive which might be of independent interest. In a nutshell, a statement hider hides an instance of a relation as a new instance in the same relation. The new instance is in the relation if and only if the initial instance is. With this building block, we can utilize existing folding schemes to construct a privacy preserving folding scheme, by first hiding each of the statements. Folding schemes allow verifying that a statement was folded into another statement, while statement hiders allow verifying that a statement was hidden as another statement.
Preserving the privacy of transactions and ensuring the regulatory compliance of transactions are two important requirements for blockchain-based financial applications. However, these two requirements are somewhat contradictory. Techniques for protecting transaction privacy, such as data encryption and zero-knowledge proof, generally make it difficult to regulate and audit the transactions. In this paper, we propose a system named zkFabLedger which enhances both the privacy and the auditability of the classic permissioned blockchain platform Hyperledger Fabric. The proposed system utilizes commitments and non-interactive zero-knowledge proofs to hide the detailed information of transactions while enabling the endorsing peer nodes to verify the regulatory compliance of transactions. Transactions are recorded on table-structured ledgers, so that the regulator can perform complex auditing of transactions. Moreover, we utilize the ring signature scheme and the secret handshake protocol to ensure the anonymity of the transaction sender while enabling the regulator to trace the senderâs identity. Simulation results demonstrate that the proposed system can balance well between privacy, regulation and efficiency.
With the rapid development of quantum computers, proofs of quantumness have recently become an interesting and intriguing research direction. However, in all current schemes for proofs of quantumness, quantum provers almost invariably face the risk of being maliciously exploited by classical verifiers. In fact, through malicious strategies in interaction with quantum provers, classical verifiers could solve some instances of hard problems that arise from the specific scheme in use. In other words, malicious verifiers can break some schemes (that quantum provers are not aware of) through interaction with quantum provers. All this is due to the lack of formalization that prevents malicious verifiers from extracting useful information in proofs of quantumness. To address this issue, we formalize zero-knowledge proofs of quantumness. Intuitively, the zero-knowledge property necessitates that the information gained by the classical verifier from interactions with the quantum prover should not surpass what can be simulated using a simulated classical prover interacting with the same verifier. As a result, the new zero-knowledge notion can prevent any malicious verifier from exploiting quantum advantage. Interestingly, we find that the classical zero-knowledge proof is sufficient to compile some existing proofs of quantumness schemes into zero-knowledge proofs of quantumness schemes. Due to some technical reason, it appears to be more general to require zero-knowledge proof on the verifier side instead of the prover side. Intuitively, this helps to regulate the verifier's behavior from malicious to be honest-but-curious. As a result, both parties will play not only one role in the proofs of quantumness but also the dual role in the classical zero-knowledge proof. Specifically, the two principle proofs of quantumness schemes: Shor's factoring-based scheme and learning with errors-based scheme in [Brakerski et al, FOCS, 2018], can be transformed into zero-knowledge proofs of quantumness by requiring an extractable non-interactive zero-knowledge argument on the verifier side. Notably, the zero-knowledge proofs of quantumness can be viewed as an enhanced security notion for proofs of quantumness. To prevent malicious verifiers from exploiting the quantum device's capabilities or knowledge, it is advisable to transition existing proofs of quantumness schemes to this framework whenever feasible.
The advancement of AI models, especially those powered by deep learning, faces significant challenges in data-sensitive industries like healthcare and finance due to the distributed and private nature of data. Federated Learning (FL) and Secure Federated Learning (SFL) enable collaborative model training without data sharing, enhancing privacy by encrypting shared intermediate results. However, SFL currently lacks effective Byzantine robustness, a critical property that ensures model performance remains intact even when some participants act maliciously. Existing Byzantine-robust methods in FL are incompatible with SFL due to the inefficiency and limitations of encryption operations in handling complex aggregation calculations. This creates a significant gap in secure and robust model training. To address this gap, we propose ByzSFL, a novel SFL system that achieves Byzantine-robust secure aggregation with high efficiency. Our approach offloads aggregation weight calculations to individual parties and introduces a practical zero-knowledge proof (ZKP) protocol toolkit. This toolkit supports widely used operators for calculating aggregation weights, ensuring correct computations without compromising data privacy. Not only does this method maintain aggregation integrity, but it also significantly boosts computational efficiency, making ByzSFL approximately 100 times faster than existing solutions. Furthermore, our method aligns with open-source AI trends, enabling plaintext publication of the final model without additional information leakage, thereby enhancing the practicality and robustness of SFL in real-world applications.
This paper proposes a three-step Secret Santa algorithm with setup that leverages Zero Knowledge Proofs (ZKP) to set up gift sender/receiver relations while maintaining the sender's confidentiality. The algorithm maintains a permutational derangement and does not require a central authority to perform successfully. The described approach can be implemented in Solidity provided the integration with a transaction relayer.
A commitment scheme is a cryptographic scheme that can verify the authenticity of the sender's input value. The verifiability of the input value is a main feature of commitment schemes suitable for sealed-bid auctions. The sealed-bid auction is a scheme of finding who is the highest bidder with the bidding price hidden. On the other hand, since the bidding value depends on the amount one can pay oneself, publishing the bidding value is as same act as publishing the amount of the user bank account. Galal and Youssef constructed a verifiable sealed-bid auction based on the homomorphic commitment scheme in 2018. In their scheme, the bidding price of the user is converted to a commitment value, and they used an interactive zero-knowledge proof to hide all users' input values in their scheme. However, their scheme uses zero-knowledge proof to guarantee the privacy-preserving. It is simpler to construct a scheme without using zero-knowledge proofs, and it is more efficient since the verification time is smaller. To overcome this problem, we propose the Privacy-Preserving Comparable Commitment Scheme (PPCCS) to identify who has the highest input value without using zero-knowledge proof. Our PPCCS uses a special encoding scheme called 0/1-Encoding to construct a commitment value, which allows Receiver (verifier) to identify who has the largest input value during the commitment phase. Moreover, our PPCCS is even more privacy-preserving than general homomorphic commitment schemes, since no other sender except Receiver can compare each commitment value. We also prove our PPCCS satisfies binding and hiding properties.
Machine learning is an important technology that can be applied to various services, such as medical diagnosis and sales data analysis. However, the machine learning often involves personal information in the data used for training, and data encryption is essential to prove the learning process without losing data privacy. Garg et al. proposed a method using zero-knowledge proofs to verify whether data owned by the prover has been correctly utilized for the training on a machine learning model. They applied their method to federated learning, in which multiple users learn a model from their data. On the other hand, their method requires each prover to share data using Multi-Party Computation (MPC), which results in significant computational overhead for each user. Furthermore, their method does not provide a solution for the case where a malicious prover proves the data that he/she has not actually used for the training. We propose a method to prove the correctness of the procedure that one's own data was used for training without using MPC by sharing models instead of sharing the data. In our scheme, each prover's model can only be accessed by two users: the verifier and the next prover, thus reducing the computational effort required for model sharing. The data and the model used for training can be applied to zero-knowledge proofs, and the verifier can verify only the model to prove that it has been trained without disclosing the data. We prove how we can identify the malicious prover when he/she sends different models to the verification phase.
This study constructs an innovative credit assessment model, combining blockchain technology with a credit scoring system to focus on solving data security and privacy protection issues. The model achieves security and transparency in the credit assessment process by designing a series of advanced algorithms and system architectures. The model uses zero-knowledge proof technology to allow users to verify their identities without revealing specific data information, thereby ensuring that privacy is not leaked. The model also introduces a distributed storage mechanism to store data on multiple nodes in a dispersed manner, preventing single point failures and improving the immutability of data. This study verifies the superiority of the model in data processing speed, assessment accuracy and system security through simulation tests. The results show that the accuracy of the model is about 15% higher than that of traditional methods, and the risk of data leakage is significantly reduced by about 20%. The blockchain credit assessment model achieves efficient and reliable credit assessment while meeting user privacy protection, providing a reference with practical value for the construction of the future credit system.
This survey provides a comprehensive examination of verifiable computing, tracing its evolution from foundational complexity theory to modern zero-knowledge succinct non-interactive arguments of knowledge (ZK-SNARKs). We explore key developments in interactive proof systems, knowledge complexity, and the application of low-degree polynomials in error detection and verification protocols. The survey delves into essential mathematical frameworks such as the Cook-Levin Theorem, the sum-check protocol, and the GKR protocol, highlighting their roles in enhancing verification efficiency and soundness. By systematically addressing the limitations of traditional NP-based proof systems and then introducing advanced interactive proof mechanisms to overcome them, this work offers an accessible step-by-step introduction for newcomers while providing detailed mathematical analyses for researchers. Ultimately, we synthesize these concepts to elucidate the GKR protocol, which serves as a foundation for contemporary verifiable computing models. This survey not only reviews the historical and theoretical advancements in verifiable computing over the past three decades but also lays the groundwork for understanding recent innovations in the field.
Kamilla Nazirkhanova, Vrushank Gunjur, X. Pilli Cruz-De Jesus, Dan Boneh
Ensuring the privacy of votes in an election is crucial for the integrity of a democratic process. Often, voting power is delegated to representatives (e.g., in congress) who subsequently vote on behalf of voters on specific issues. This delegation model is also widely used in Decentralized Autonomous Organizations (DAOs). Although several existing voting systems used in DAOs support private voting, they only offer public delegation. In this paper, we introduce Kite, a new protocol that enables $\textit{private}$ delegation of voting power for DAO members. Voters can freely delegate, revoke, and re-delegate their power without revealing any information about who they delegated to. Even the delegate does not learn who delegated to them. The only information that is recorded publicly is that the voter delegated or re-delegated their vote to someone. Kite accommodates both public and private voting for the delegates themselves. We analyze the security of our protocol within the Universal Composability (UC) framework. We implement Kite as an extension to the existing Governor Bravo smart contract on the Ethereum blockchain, that is widely used for DAO governance. Furthermore, we provide an evaluation of our implementation that demonstrates the practicality of the protocol. The most expensive operation is delegation due to the required zero-knowledge proofs. On a consumer-grade laptop, delegation takes between 7 and 167 seconds depending on the requested level of privacy.
Cryptographic algorithms form the backbone of decentralized finance (DeFi), ensuring secure, transparent, and tamper-proof transactions in blockchain ecosystems. These algorithms enable critical functionalities such as digital signatures, consensus mechanisms, and privacy preservation, empowering decentralized platforms to operate without intermediaries. This paper explores the role of cryptographic techniques, including asymmetric encryption, hash functions, and zero-knowledge proofs, in enabling DeFi applications like smart contracts, tokenization, and decentralized exchanges. Additionally, it examines emerging trends, such as post-quantum cryptography, to address vulnerabilities posed by quantum computing advancements. By investigating the intersection of cryptography and DeFi, this study highlights the challenges and opportunities in enhancing security, scalability, and interoperability within decentralized systems.
This thesis primarily explores efficient zero-knowledge range arguments as first part and privacy-preserving applications within distributed systems as second part. The first part focuses on zero-knowledge range arguments, a fundamental cryptographic primitive that enables a prover to convince a verifier that a secret value lies within a predefined range without disclosing any unnecessary information. However, deploying range arguments in practice faces significant challenges due to high gas costs and computational overhead. This part contributes to optimizing the {\em verification efficiency} of range arguments to reduce deployment costs on blockchains and other decentralized platforms. We introduce four new zero-knowledge range arguments in the discrete logarithm setting that require only $c \sqrt{\tfrac{N}{\log N}}$ group exponentiations in verification, where $N$ is the number of bits to represent a range and $c$ is a small constant. These improvements make the arguments concretely efficient for blockchain deployment with minimal gas costs. The second part proposes two privacy-enhancing secure distributed systems and applications. First, we introduce a novel paradigm for decentralized privacy-preserving group purchasing for energy plans. Leveraging privacy-preserving blockchain technology and secure multi-party computation, this approach enables users to form coalitions for coordinated switch decisions in a decentralized manner without relying on a trusted third party. We develop an effective solution to support decentralized privacy-preserving group purchasing, which includes a competitive online algorithm for decision-making, secure multi-party computation for enhancing privacy, and zero-knowledge proofs on the blockchain for verifying the private input data used in our online algorithm. Second, we propose a novel scheme zk-qrcode based on anonymous credentials and zk-SNARK. Our scheme leverages the following features: Blockchain-based credential issuance, we eliminate the need for credential issuers to hold signing keys by allowing them to issue credentials to a smart contract on the blockchain; Flexible and composable identity statements, enables users to prove complex statements about their credentials without revealing unnecessary information; QR code based verification: enables user interaction with service providers through QR codes displayed or scanned on mobile phones, incorporating identity proof and access control requests. We implement and evaluate our zk-qrcode scheme in practical use cases for entering bar anonymously. Our results demonstrate that the scheme is efficient and practical, with access control proof generation and verification taking less than 650 milliseconds.
Ahmed Ayoub Bellachia, Mouhamed Amine Bouchiha, Yacine Ghamri-Doudane, Mourad Rabah
Blockchain-based Federated Learning (BFL) is an emerging decentralized machine learning paradigm that enables model training without relying on a central server. Although some BFL frameworks are considered privacy-preserving, they are still vulnerable to various attacks, including inference and model poisoning. Additionally, most of these solutions employ strong trust assumptions among all participating entities or introduce incentive mechanisms to encourage collaboration, making them susceptible to multiple security flaws. This work presents VerifBFL, a trustless, privacy-preserving, and verifiable federated learning framework that integrates blockchain technology and cryptographic protocols. By employing zero-knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) and in-crementally verifiable computation (IVC), VerifBFL ensures the verifiability of both local training and aggregation processes. The proofs of training accuracy and aggregation are verified on-chain, guaranteeing the integrity and auditability of each participant's contributions. To protect training data from inference attacks, VerifBFL leverages differential privacy. Finally, to demonstrate the efficiency of the proposed protocols, we built a proof of concept using emerging tools. The results show that generating proofs for local training and aggregation in VerifBFL takes less than 81s and 2s, respectively, while verifying them on-chain takes less than 0.6s.
Marlena Broniszewska, Wiktor B. Daszczuk, Denny B. Czejdo
Global digitalization has accelerated, leading to continuous online shopping and services growth. However, the centralized nature of traditional e-commerce platforms raises concerns about data control, privacy, and potential single points of failure. Blockchain technology offers a decentralized alternative that addresses these issues, improving online transactions with enhanced privacy and anonymity for users. This article explores the problem of anonymization in web services by developing an anonymous online auction system using blockchain and zero-knowledge proof (ZKP). We propose a solution that employs ZKP in two stages: first, to verify that a user is eligible to participate in the auction, thereby creating a list of potential bidders; second, to prove that a bid is placed by a user from this list without disclosing their identity. This approach allows participants to engage in auctions anonymously, protecting their interests from competitors and sellers. The system eliminates the need for a trusted third party by leveraging the blockchain immutable ledger for transparency, giving users full control over their data and participation. We present the architecture and implementation details of the system, demonstrating its feasibility and potential to enhance privacy and security in online auctions.
Distributed ledger technology offers several advantages for banking and finance industry, including efficient transaction processing and cross-party transaction reconciliation. The key challenges for adoption of this technology in financial institutes are (a) the building of a privacy-preserving ledger, (b) supporting auditing and regulatory requirements, and (c) flexibility to adapt to complex use-cases with multiple digital assets and actors. This paper proposes a framework for a private, audit-able, and distributed ledger (PADL) that adapts easily to fundamental use-cases within financial institutes. PADL employs widely-used cryptography schemes combined with zero-knowledge proofs to propose a transaction scheme for a `table' like ledger. It enables fast confidential peer-to-peer multi-asset transactions, and transaction graph anonymity, in a no-trust setup, but with customized privacy. We prove that integrity and anonymity of PADL is secured against a strong threat model. Furthermore, we showcase three fundamental real-life use-cases, namely, an assets exchange ledger, a settlement ledger, and a bond market ledger. Based on these use-cases we show that PADL supports smooth-lined inter-assets auditing while preserving privacy of the participants. For example, we show how a bank can be audited for its liquidity or credit risk without violation of privacy of itself or any other party, or how can PADL ensures honest coupon rate payment in bond market without sharing investors values. Finally, our evaluation shows PADL's advantage in performance against previous relevant schemes.
With the rapid development of the blockchain industry and the widespread adoption of IoT devices, which are often deployed on different blockchains, the need for cross-chain value and data exchange has become increasingly important. However, existing cross-chain transactions face challenges such as low efficiency, high costs, and insufficient security. To address these issues, this paper proposes a cross-chain transaction scheme based on aggregated zero-knowledge proofs. This scheme optimizes the allocation of computing resources in a distributed environment and employs a multi-branch balanced Merkle tree to construct aggregated zero-knowledge proofs, significantly reducing the verification costs for batch cross-chain transactions.To further enhance data privacy and integrity, this paper introduces the Secure Aggregated Block Verification (SABV) algorithm and improves system consistency and reliability through the Local Merkle Tree Rebalance (LMTR) algorithm. In addition, this paper analyzes the basic security of the proposed scheme when implemented in adversarial environments and provides countermeasures for common threats in distributed systems. Finally, simulations and actual deployment on the Ethereum test network were conducted. The results indicate that our method reduces CPU usage, memory consumption, and time expenditure by 50.10%, 99.03%, and 99.47%, respectively, during the generation of zero-knowledge proofs for batch cross-chain transactions. At the same time, building upon the performance improvements of the existing zero-knowledge proofs, our approach also demonstrates significant enhancements in contract deployment and cross-chain transaction efficiency.
It is well known that a Bose-Einstein (BE) condensate of atoms exists in a system of interacting Bose atoms at $T\lesssim T^{(i)}_{c}$, where $T^{(i)}_{c}$ is the BE condensation temperature of an ideal gas. It is also generally accepted that BE condensation is impossible at ``ultrahigh'' temperatures $T\gg T^{(i)}_{c}$. While the latter property has been theoretically proven for an ideal gas, no such proof exists for an interacting system, to our knowledge. In this paper, we propose an approximate mathematical proof for a finite, nonrelativistic, periodic system of $N$ spinless interacting bosons. The key point is that, at $T\gg T^{(i)}_{c}$, the main contribution to the occupation number $N_{0}=\frac{1}{Z}\sum_{\wp}e^{-E_{\wp}/k_{B}T}\langle Ψ_{\wp}|\hat{a}^{+}_{\mathbf{0}}\hat{a}_{\mathbf{0}}|Ψ_{\wp}\rangle$, corresponding to atoms with zero momentum, originates from the states containing $N$ elementary quasiparticles. These states do not contain the BE condensate of zero-momentum atoms, implying that an ultrahigh temperature should ``blur'' such a condensate.
Sivamuganthan A L Mohana Dass, Sai Rama Mahalingam, Phung Li Hang, Kelvin Kelvin ¡ 12 authors
Blockchain technology has emerged as a very revolutionary tool facilitating secure and transparent digital interactions which is driven by its decentralised structure, cryptographic methods, and consensus protocols. There are a lot of substantial advantages such as improved confidentiality, integrity, and availability of blockchain security. But it also comes with a few drawbacks such as scalability issues, vulnerabilities system and system failures. This study examines the current trend of blockchain technology, its strengths and limitations, and its potential and proposes strategic improvements to enhance its resilience and scalability. The proposed advancement encompasses the integration of artificial intelligence (AI) and machine learning (ML) for real-time threat detection, the adoption of sophisticated cryptographic techniques like Zero-Knowledge Proofs (ZKPs) and quantum-resistant algorithms, as well as the implementation of adaptive consensus mechanisms aimed at optimising resource utilisation. These enhancements are pivotal for overcoming most of the drawbacks in blockchain security and discovering its complete potential. Through ongoing interdisciplinary exploration and innovation, blockchain has a very high potential to revolutionise all industries by providing strong security, enhancing operational efficiency, and ensuring scalability, thus creating secure, reliable, and decentralised digital ecosystems.
ABSTRACT This paper deals with the twoâplayer zeroâsum game problem, which is a bounded âgain robust control problem. Finding an analytical solution to the complex HamiltonâJacobiâIssacs (HJI) equation is a challenging task. Hence, a novel Qâlearning algorithm for unknown continuousâtime (CT) affineâinâinputs nonlinear systems is proposed for generating an approximate solution to the HJI equation, which is valid in a local domain due to the use of a local approximator, that is, a Neural Network (NN) structure. The approach is modelâfree and does not require the knowledge of system drift dynamics, and input and disturbance gains. The algorithm learns online from measurements of state variables in real time. To generate the local approximate solution of the HJI equation for the twoâplayer zeroâsum game problem for nonlinear systems, the proposed nonâiterative algorithm requires only a single critic NN instead of the commonly used triple NN approximator structure. A persistence of excitation condition is required to guarantee Uniformly Ultimately Boundedness (UUB) and convergence to the optimal solution. The effectiveness of the proposed Qâlearning approach for the twoâplayer zeroâsum game problem is demonstrated via simulations of a linear Fâ16 aircraft plant and a highly complex nonlinear system. Proof of closedâloop system stability is provided using Lyapunov Analysis, and convergence of the approximate solution to the true saddleâpoint solution is guaranteed in a UUBâsense.
The cross-chain identity authentication method based on relay chains provides a promising solution to the issues brought by the centralized notary mechanism. Nonetheless, it continues to encounter numerous challenges regarding data privacy, security, and issues of heterogeneity. For example, there is a concern regarding the protection of identity information during the cross-chain authentication process, and the incompatibility of cryptographic components across different blockchains during cross-chain transactions. We design and propose a cross-chain identity privacy protection method based on relay chains to address these issues. In this method, the decentralized nature of relay chains ensures that the cross-chain authentication process is not subject to subjective manipulation, guaranteeing the authenticity and reliability of the data. Regarding the compatibility issue, we unify the user keys according to the identity manager organization, storing them on the relay chain and eliminating the need for users to configure identical key systems. Additionally, to comply with General Data Protection Regulation (GDPR) principles, we store the user keys from the relay chain in distributed servers using the InterPlanetary File System (IPFS). To address privacy concerns, we enable pseudonym updates based on the userâs public key during cross-chain transactions. This method ensures full compatibility while protecting user privacy. Moreover, we introduce Zero-Knowledge Proof (ZKP) technology, ensuring that audit nodes cannot trace the userâs identity information with malicious intent. Our method offers compatibility while ensuring unlinkability and anonymity through thorough security analysis. More importantly, comparative analysis and experimental results show that our proposed method achieves lower computational cost, reduced storage cost, lower latency, and higher throughput. Therefore, our method demonstrates superior security and performance in cross-chain privacy protection.
Open access
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
John Stephan, Matej Pavlovic, Antonio Locascio, Benjamin Livshits
Zero-Knowledge (ZK) rollups have become a popular solution for scaling blockchain systems, offering improved transaction throughput and reduced costs by aggregating Layer 2 transactions and submitting them as a single batch to a Layer 1 blockchain. However, the computational burden of generating validity proofs, a key feature of ZK rollups, presents significant challenges in terms of performance and decentralization. Current solutions rely on centralized infrastructure to handle the computational tasks, limiting the scalability and decentralization of rollup systems. This paper proposes CrowdProve, a prover orchestration layer for outsourcing computation to unreliable commodity hardware run by a broad community of small provers. We apply CrowdProve to proving transaction batches for a popular ZK rollup. Through our experimental evaluation, we demonstrate that community proving can achieve performance comparable to, and in some cases better than, existing centralized deployments. Our results show that even systems utilizing modest hardware configurations can match the performance of centralized solutions, making community-based proof generation a viable and cost-effective alternative. CrowdProve allows both the rollup operator and community participants to benefit: the operator reduces infrastructure costs by leveraging idle community hardware, while community provers are compensated for their contributions.
The Bitcoin white paper introduced blockchain technology, enabling trustful transactions without intermediaries. Smart contracts emerged with Ethereum and blockchains expanded beyond cryptocurrency, applying to auctions, crowdfunding and electronic voting. However, blockchain's transparency raised privacy concerns and initial anonymity measures proved ineffective. Smart contract privacy solutions employed zero-knowledge proofs, homomorphic encryption and trusted execution environments. These approaches have practical drawbacks, such as limited functionality, high computation times and trust on third parties requirements, being not fully decentralized. This work proposes a solution utilizing zk-SNARKs to provide privacy in smart contracts and blockchains. The solution supports both fungible and nonfungible tokens. Additionally, the proposal includes a new type of transactions, called delegated transactions, which enable use cases like Delivery vs Payment (DvP).
The Federated Unified Secure Exchange (FUSE) algorithm represents a breakthrough in computer science and cybersecurity, building upon three foundational elements: Homomorphic Encryption, Zero-Knowledge Proofs, and Federated Learning. By deeply integrating these three pillars, FUSE not only guarantees data integrity and privacy in decentralized environments but also maintains high performance and remarkable scalability. This paper details the architecture, mathematical principles, and real-world applications of FUSE across Internet of Things (IoT), healthcare, finance, and supply chains. In addition, it compares FUSE with several âclassicâ algorithms that have dramatically changed the worldâsuch as QuickSort, RSA, Dijkstra, PageRank, FFT, Backpropagation, SHA, Monte Carlo, Gradient Descent, and Blockchainâhighlighting how FUSE addresses existing limitations in data protection and distributed computation.
The trading of data is becoming increasingly important as it holds substantial value. A blockchain-based data marketplace can provide a secure and transparent platform for data exchange. To facilitate this, developing a fair data exchange protocol for digital goods has garnered considerable attention in recent decades. The Zero Knowledge Contingent Payment (ZKCP) protocol enables trustless fair exchanges with the aid of blockchain and zero-knowledge proofs. However, applying this protocol in a practical data marketplace is not trivial. In this paper, several potential attacks are identified when applying the ZKCP protocol in a practical public data marketplace. To address these issues, we propose SmartZKCP, an enhanced solution that offers improved security measures and increased performance. The protocol is formalized to ensure fairness and secure against potential attacks. Moreover, SmartZKCP offers efficiency optimizations and minimized communication costs. Evaluation results show that SmartZKCP is both practical and efficient, making it applicable in a data exchange marketplace.