Succinct arguments are proof systems that allow a powerful, but untrusted, prover to convince a weak verifier that an input x belongs to a language \(L \in \mathsf {NP}\) , with communication that is much shorter than the \(\mathsf {NP}\) witness. Such arguments, which grew out of the theory literature, are now drawing immense interest also in practice, where a key bottleneck that has arisen is the high computational cost of proving correctness. In this work, we address this problem by constructing succinct arguments for general computations, expressed as Boolean circuits (of bounded fan-in), with a strictly linear size prover. The soundness error of the protocol is an arbitrarily small constant. Prior to this work, succinct arguments were known with a quasi- linear size prover for general Boolean circuits or with linear-size only for arithmetic circuits, defined over large finite fields. In more detail, for every Boolean circuit \(C=C(x,w)\) , we construct an \(O(\log |C|)\) -round argument-system in which the prover can be implemented by a size \(O(|C|)\) Boolean circuit (given as input both the instance x and the witness w ), with arbitrarily small constant soundness error and using \(\mathrm{poly}(\lambda ,\log |C|)\) communication, where \(\lambda\) denotes the security parameter. The verifier can be implemented by a size \(O(|x|) + \mathrm{poly}(\lambda , \log |C|)\) circuit following a size \(O(|C|)\) private pre-processing step, or, alternatively, by using a purely public-coin protocol (with no pre-processing) with a size \(O(|C|)\) verifier. The protocol can be made zero-knowledge using standard techniques (and with similar parameters). The soundness of our protocol is computational and relies on the existence of collision resistant hash functions that can be computed by linear-size circuits, such as those proposed by Applebaum et al. (ITCS, 2017). At the heart of our construction is a new information-theoretic interactive oracle proof ( \(\mathsf {IOP}\) ), an interactive analog of a \(\mathsf {PCP}\) , for circuit satisfiability, with constant prover overhead. The improved efficiency of our \(\mathsf {IOP}\) is obtained by bypassing a barrier faced by prior \(\mathsf {IOP}\) constructions, which needed to (either explicitly or implicitly) encode the entire computation using a multiplication code.
Multi-scalar multiplication (MSM) is the primary building block in many pairing-based zero-knowledge proof (ZKP) systems. MSM at large scales has become the main bottleneck in ZKP implementations. Inspired by existing SIMD-accelerated work, we are focused on accelerating MSM computing efficiency using SIMD instructions in a single CPU environment. First, we propose a SIMD-accelerated MSM computing architecture with no write conflicts and constant memory overheads. This architecture utilizes multithreading to achieve task-level and loop-level parallelism and employs a three-tier buffer mechanism to maximize the utilization of the SIMD engine. Instanced with AVX512-IFMA instructions, we implement six SIMD elliptic curve arithmetic engines for different point addition in three coordinate systems and two groups. Moreover, we integrate our AVX-MSM implementation into the libsnark library, naming it AVX-ZK. In more detail, point deduplication and “Three-Stage” memory optimization are proposed to address problems existing in practical applications. Based on the RELIC library, our performance results on the BLS12-381 curve show that our AVX-MSM achieves up to 27.86x speedup over the most popular Pippenger algorithm. Compared with libsnark, our AVX-ZK implementation achieves over 11.53x (up to 20.26x) speedup under standard benchmarks.
Xander Pottier, Thomas De Ruijter, Jonas Bertels, Wouter Legiest · 6 authors
The Multi-Scalar Multiplication (MSM) is the main barrier to accelerating Zero-Knowledge applications. In recent years, hardware acceleration of this algorithm on both FPGA and GPU has become a popular research topic and the subject of a multi-million dollar prize competition (ZPrize). This work presents OPTIMSM: Optimized Processing Through Iterative Multi-Scalar Multiplication. This novel accelerator focuses on the acceleration of the MSM algorithm for any Elliptic Curve (EC) by improving upon the Pippenger algorithm. A new iteration technique is introduced to decouple the required buckets from the window size, resulting in fewer EC computations for the same on-chip memory resources. Furthermore, we combine known optimizations from the literature for the first time to achieve additional latency improvements. Our enhanced MSM implementation significantly reduces computation time, achieving a speedup of up to x12.77 compared to recent FPGA implementations. Specifically, for the BLS12-381 curve, we reduce the computation time for an MSM of size 224 to 914 ms using a single compute unit on the U55C FPGA or to 231 ms using four U55C devices. These results indicate a substantial improvement in efficiency, paving the way for more scalable and efficient Zero-Knowledge proof systems.
With the wide application of electric vehicles, smart robots and Internet of Things (IoT) devices, efficient scheduling of mobile charging systems has become an important research direction in smart energy management. However, the traditional cloud computing architecture is difficult to meet the requirements of low latency, high reliability and privacy protection, and the existing scheduling strategies still have challenges in terms of energy optimization, task balancing and dynamic adaptability. To this end, this paper proposes an intelligent mobile charging scheduling method that integrates edge computing and biomechanical modeling, constructs a biomechanical-based charging demand modeling and energy consumption analysis framework, and combines bionic optimization algorithms to achieve efficient path planning. Meanwhile, an edge computing architecture is adopted to optimize resource scheduling, and a federated learning mechanism is designed to enhance cross-domain data processing capability. To safeguard user privacy, a multi-level privacy protection mechanism is proposed, combining differential privacy, homomorphic encryption and zero-knowledge proof to ensure data security. Experimental results show that the method outperforms traditional methods in terms of task response time, energy consumption optimization, load balancing and privacy security, and can significantly improve the charging scheduling efficiency and provide effective technical support for large-scale distributed charging networks. The research results provide a theoretical basis and engineering practice reference for the application of smart charging networks, edge intelligent computing and privacy protection technology.
A. Raji Reddy, K. Jayasurya, Papisetty Pavan Kalyan
The rapid adoption of e-learning has raised concerns about online exam security, transparency, and scalability. We introduce DeCentralEx, a decentralized hybrid system that verifies blockchain-based smart contracts and stores them in encrypted cloud storage for secure and rapid scrutiny. It reduces on-chain data dependencies by using Ethereum smart contracts for decentralized role validation and Firebase Firestore for AES-encrypted questions and answers. Thus, DeCentralEx addresses the drawbacks of blockchain models, which have high gas fees, latency, and low concurrency. Its deployment and testing on the Sepolia testnet show good tamper resistance, secure data handling, and automatic result processing. Compared to entirely blockchain-based systems, its hybrid architecture, with a calibrated design, offers great scalability and prevents disruptions during periods of high demand. Comparative performance testing reveals that DeCentralEx strikes a balance between security, affordability, and scalability. Such testing confirmed its capacity to handle high concurrency with low gas usage compared to on-chain solutions. The study's aims were confirmed, proving an implementable and pragmatic approach for online exam security. Zero-knowledge proofs and Layer 2 blockchain technology could boost efficiency and decentralization. Decentralized architecture has the potential to transform digital testing environments in academic institutions worldwide.
P. Selvaraj, A. Hyils Sharon Magdalene, Suresh Sankaranarayanan, Alias Muralidharan R. Rengaraj · 7 authors
This work designs a novel algorithm to address the pressing security challenges anticipated in 6G networks. A combination of AES, zero-knowledge proofs, and RSA algorithms offers a robust framework for enhancing data security and privacy in advanced wireless communication systems. AES and RSA, renowned for their encryption capabilities, are integrated for secure data transmission and key exchange processes in 6G networks. Moreover, the incorporation of zero-knowledge proofs adds an additional layer of security, allowing entities to validate their knowledge without compromising sensitive information. Through extensive simulations and analyses, the effectiveness of the proposed algorithm to ensure secure communication within 6G networks is demonstrated. The algorithm is able to reduce security threats and vulnerabilities. This research lays the groundwork for the development of resilient and trustworthy next-generation communication infrastructures. Finally, the integration of AES, RSA, and zero-knowledge proofs presents a favorable approach to strengthen data security in 6G networks, paving the way for more reliable and secure wireless communication technologies in the future.
This Blockchain technology has revolutionized various industries by offering decentralized, secure, and immutable record-keeping. However, its adoption faces challenges such as scalability, energy consumption, and interoperability. As blockchain continues to evolve, researchers and developers have been working on innovative solutions to address these limitations and expand its practical applications. This paper explores recent advancements aimed at enhancing blockchain technology, focusing on key areas such as scalability solutions, energy-efficient consensus mechanisms, and interoperability protocols. It delves into techniques like sharding, Layer 2 solutions, and optimized consensus algorithms that improve transaction speed and reduce congestion. Additionally, it examines alternative consensus mechanisms like Proof-of-Stake (PoS) and Proof-of-Authority (PoA), which offer sustainability and efficiency without compromising security. Furthermore, the paper investigates interoperability solutions that enable seamless data exchange between different blockchain networks, such as atomic swaps, cross-chain communication protocols, and blockchain bridges. The study also highlights emerging trends that are set to shape the future of blockchain, including quantum-resistant cryptography, AI integration, Zero-Knowledge Proofs (ZKPs), and Blockchain-as-a-Service (BaaS). By addressing these enhancements, blockchain technology can achieve greater adoption, enabling new opportunities across various industries such as finance, supply chain, healthcare, governance, and IoT. The paper concludes with an analysis of the broader impact of blockchain innovations, emphasizing the need for continuous research and development to overcome existing barriers and unlock its full potential in modern digital infrastructure.
Recently, designing symmetric primitives for applications in cryptographic protocols including multi-party computation, fully homomorphic encryption, and zero-knowledge proofs has become an important research topic. Among many such new symmetric schemes, a power function over a large finite field$\mathbb{F}_{q}$is commonly used. In this paper, we revisit the algebraic degree's growth for a substitution-permutation network (SPN) cipher over$\mathbb{F}_{2^{n}}(n\geq 3)$, whose S-box is defined as a composition of a power function$P(x)=x^{2^{d}+1}$where$d\geq 1$with a polynomial$A(x)=a_{0}+ \sum\limits_{w=1}^{W}a_{w}x^{2^{\beta_{w}}}$where$a_{i}\in \mathbb{F}_{2^{n}}$for$0\leq i\leq W$and$a_{w}\neq 0$for$1\leq w\leq W$. We propose a new coefficient grouping technique, which is based on our new description of the monomials that will probably appear in the state. Specifically, we propose a new measure to find proper$(\beta_{1},\beta_{2}, \ldots,\beta_{W})$for the algebraic degree's fastest growth and a new method to compute the algebraic degree's upper bound for arbitrary$A(x)$. Especially for Chaghri, which was presented at ACM CCS 2022, we obtained a tighter upper bound on the algebraic degree.
Abstract Privacy is one of the major security concerns. The zero-knowledge proof enables the transmission of data from the sender to the receiver without disclosing the actual content of the data. The proposed work uses the ZK-STARK (Zero-Knowledge Scalable Transparent ARgument of Knowledge) Algorithm for transaction privacy in the organic jaggery supply chain. The paper emphasizes a detailed mathematical model, involving two key participants: the prover (food processor) and the verifier (distributor). The prover calculates the polynomial for the problem, its composition polynomial, and provides its Merkle proof to the verifier. The verifier conducts queries to confirm and validate the accuracy of the information. Using the fast reed-solomon interactive oracle proofs protocol, the proof is validated. It measures performance as proof generation and verification time, proof size, and throughput. Plans involve increasing the domain size of this algorithm, varying the polynomial interpolation, and evaluating its performance measures by integrating it into Blockchain.
Open access
Blockchain Technology Applications and Security
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
This is the NIST Threshold Call, calling for public submissions of multi-party threshold schemes, and other related crypto-systems, to support the United States’ National Institute of Standards and Technology (NIST) in gathering a public body of reference materials unadvanced cryptography. In a threshold scheme, a reference cryptographic primitive (e.g., signing, encryption, decryption, key generation) is computed in a distributed manner, while its private/secret key is or becomes secret-shared across various parties. The threshold schemes submitted in reply to this call will be interchangeable with a reference no threshold primitive of interest, in the sense that their outputs can be used interchangeably in a subsequent operation. The primitives of interest are organized into various categories, across two classes: Class N, for selected NIST-specified primitives; and Class S, for special primitives that are not specified by NIST but are threshold friendly or have useful functional features. The scope of Class S also includes fully homomorphic encryption, zero-knowledge proofs, and auxiliary gadgets. This document specifies submission phases, and the requirements for submitting a package, including a technical specification, a reference implementation, and a report on experimental evaluation. A subsequent phase of public analysis will support the elaboration of a characterization report, which may help assess new interests beyond the cryptographic techniques currently standardized by NIST, and may include recommendations for future processes.
Zhengbang Yang, Lutan Zhao, Peinan Li, Han Liu · 8 authors
Zero-knowledge proof (ZKP) allows a prover to convince a verifier of the truth of a statement without revealing any secret information. This property is utilized in numerous privacy-preserving applications. However, the huge overhead of proof generation impedes the widespread adoption of ZKP. As a result, many ZKP accelerators have been developed to speed up proof generation. However, existing accelerators are designed at the granularity of core operators and exhibit low hardware resource utilization and limited adaptability. In this paper, we identify the commonality of all computation stages in proof generation at the level of basic finite field arithmetic operations. Based on this insight, we propose LegoZK, a dynamically reconfigurable hardware accelerator for ZKP. LegoZK employs finite field arithmetic units (FAUs) as its fundamental components and integrates these FAUs with a hierarchical on-chip network (NoC). By dynamically configuring the FAUs and the NoC, LegoZK can effectively accelerate the entire proof generation process, achieving higher overall performance. Additionally, for the most time-consuming MSM, this paper proposes a fast, fully pipelined bucket reduction algorithm based on lookup tables, which significantly reduces the latency of MSM. Experimental results demonstrate that LegoZK achieves on average speedup of $31.96 \times$ and $11.30 \times$ in proof generation compared to the state-of-the-art ZKP ASIC accelerator PipeZK and the GPU accelerator GZKP, respectively. And compared to PipeZK, LegoZK achieves $\mathbf{5 0. 1 \%}$ area reduction and $\mathbf{3 7. 7 \%}$ power consumption reduction.
The adoption of e-government services has transformed public administration by providing digital access to government resources and services. However, ensuring the privacy and security of citizens' sensitive data remains a critical challenge. Privacy-enhancing cryptographic techniques offer promising solutions to safeguard data confidentiality, integrity, and user anonymity in e-government applications. This article explores various cryptographic methods such as homomorphic encryption, zero-knowledge proofs, and secure multi-party computation that can bolster privacy in digital government services. We present a comprehensive analysis of their applicability, strengths, and limitations within e-government frameworks. Additionally, a detailed graph illustrates the comparative efficiency and privacy guarantees of these techniques in practical deployment. The study concludes with recommendations for integrating advanced cryptography to enhance trust, transparency, and compliance in secure digital governance.
This publication presents a novel cryptographic commit scheme named DFTWS which is used to enable deterministic, fair, and transparent winner selection in an open source Proof-of-Useful-Work blockchain for High Energy Physics (HEP) called gophy. In gophy, instead of spamming hashing operations to mine blocks, miners are running computationally expensive Monte Carlo simulations to support a real-world HEP experiment with necessary data required to conduct the experiment. To preserve the usefulness property, block problems are defined over time by a Root Authority which is coordinated by a representative of a real-world HEP experiment. In order to be able to provide a transparent mechanism that allows for fair block winner selection from a list of eligible miners that solved a block problem, DFTWS is employed to achieve consensus between nodes. A strength of this approach is that every node is able to verify the fairness of the winner selection process. This publication provides an in-depth description and theoretical fairness analysis of DFTWS, a practical evaluation of its performance under real-world conditions and considerations of potential bottlenecks that can potentially occur as the node network scales. It also discusses a deployment strategy for using DFTWS on top of existing blockchain infrastructure like the Ethereum network. Additionally, theoretical performance aspects of DFTWS are compared with various state-of-the-art cryptographic commitment schemes and Zero-Knowledge Proof systems.
Active Learning (AL) is a machine learning technique where the model selectively queries the most informative data points for labeling by human experts. Integrating AL with crowdsourcing leverages crowd diversity to enhance data labeling but introduces challenges in consensus and privacy. This poster presents CrowdAL, a blockchain-empowered crowd AL system designed to address these challenges. CrowdAL integrates blockchain for transparency and a tamper-proof incentive mechanism, using smart contracts to evaluate crowd workers' performance and aggregate labeling results, and employs zero-knowledge proofs to protect worker privacy.
Dong Liu, Juan S. Giraldo, Peter Pálenský, Pedro P. Vergara
Model-free power flow calculation, driven by the rise of smart meter (SM) data and the lack of network topology, often relies on artificial intelligence neural networks (ANNs). However, training ANNs require vast amounts of SM data, posing privacy risks for households in distribution networks. To ensure customers' privacy during the SM data gathering and online sharing, we introduce a privacy preserving PF calculation framework, composed of two local strategies: a local randomisation strategy (LRS) and a local zero-knowledge proof (ZKP)-based data collection strategy. First, the LRS is used to achieve irreversible transformation and robust privacy protection for active and reactive power data, thereby ensuring that personal data remains confidential. Subsequently, the ZKP-based data collecting strategy is adopted to securely gather the training dataset for the ANN, enabling SMs to interact with the distribution system operator without revealing the actual voltage magnitude. Moreover, to mitigate the accuracy loss induced by the seasonal variations in load profiles, an incremental learning strategy is incorporated into the online application. The results across three datasets with varying measurement errors demonstrate that the proposed framework efficiently collects one month of SM data within one hour. Furthermore, it robustly maintains mean errors of 0.005 p.u. and 0.014 p.u. under multiple measurement errors and seasonal variations in load profiles, respectively.
Fatemeh Haghighi, Michael B. Gotway, Jianming Liang
Human experts demonstrate proficiency not only in disentangling anatomical structures from disease conditions but also in intertwining anatomical and disease information to accurately diagnose a variety of disorders. However, deep learning models, despite their prowess in acquiring intricate representation, often struggle to learn representation where distinct semantic aspects of the data (both anatomy and pathology) are entangled, particularly in medical images, which present a rich array of anatomical structures and potential pathological conditions. We envision that a deep model, when trained to comprehend medical images akin to human perception, would offer powerful representation with higher generalizability, robustness, and interpretability. To realize this vision, we have developed LeADER, a framework for learning anatomy-disease entangled representation from medical images. As a proof of concept, we have trained LeADER on ≈IM chest radiographs gatheredfrom 10 public datasets. Experimental results across 11 medical tasks, compared to 8 baselines in zero-shot, linear probing, limited data regimes, and full fine-tuning settings, demonstrate LeADER's superior performance over the Google CXR Foundation Model, large-scale medical models, and fully/self-supervised baselines across diverse downstream tasks. This enhanced performance is attributed to the significance of entangling anatomy-specific and disease-specific representations via our framework, which enables the simultaneous acquisition of both anatomical and disease knowledge, yet overlooked in existing supervised/self-supervised learning methods. All code and models are available at GitHub.com/JLiangLab/LeADER.
Abstract As machine learning technologies advance rapidly across various domains, concerns over data privacy and model security have grown significantly. These challenges are particularly pronounced when models are trained and deployed on cloud platforms or third-party servers due to the computational resource limitations of users’ end devices. In response, zero-knowledge proof (ZKP) technology has emerged as a promising solution, enabling effective validation of model performance and authenticity in both training and inference processes without disclosing sensitive data. Thus, ZKP ensures the verifiability and security of machine learning models, making it a valuable tool for privacy-preserving AI. Although some research has explored the verifiable machine learning solutions that exploit ZKP, a comprehensive survey and summary of these efforts remains absent. This survey paper aims to bridge this gap by reviewing and analyzing all the existing Zero-Knowledge Machine Learning (ZKML) research from June 2017 to August 2025. We begin by introducing the concept of ZKML and outlining its ZKP algorithmic setups under three key categories: verifiable training, verifiable inference, and verifiable testing. Next, we provide a comprehensive categorization of existing ZKML research within these categories and analyze the works in detail. Furthermore, we explore the implementation challenges faced in this field and discuss the improvement works to address these obstacles. Additionally, we highlight several commercial applications of ZKML technology. Finally, we propose promising directions for future advancements in this domain.
With the widespread adoption of Zero-Knowledge Proof systems, particularly ZK-SNARK, the efficiency of proof generation, encompassing both the witness generation and proof computation phases, has become a significant concern. While substantial efforts have successfully accelerated proof computation, progress in optimizing witness generation remains limited, which inevitably hampers overall efficiency. In this paper, we propose Yoimiya, a scalable framework with pipeline, to optimize the efficiency in ZK-SNARK systems. First, Yoimiya introduces an automatic circuit partitioning algorithm that divides large circuits of ZK-SNARK into smaller subcircuits, the minimal computing units with smaller memory requirement, allowing parallel processing on multiple units. Second, Yoimiya decouples witness generation from proof computation, and achieves simultaneous executions over units from multiple circuits. Moreover, Yoimiya enables each phase scalable separately by configuring the resource distribution to make the time costs of the two phases aligned, maximizing the resource utilization. Experimental results confirmed that our framework effectively improves the resource utilization and proof generation speed.
Hongmei Ma, Yifan Liu, Yi Liu, Fan Feng · 5 authors
The vast number of IoT devices is distributed across multiple trust domains, each with distinct security policies, trust models, and permission management methods. This diversity increases the risk of privacy exposure during cross-domain communications. At the same time, traditional authentication methods have problems, such as complex certificate management, high risk of key escrow, and reliance on trusted third parties. To address the above problems, this article proposes a novel method, enhancing device privacy through certificateless encryption and zero-knowledge proof (DPCZK). DPCZK achieves decentralization by leveraging a consortium blockchain as a trust bridge across different domains. The adoption of certificateless encryption mitigates the incomplete trust issues associated with the key generation center. Furthermore, DPCZK incorporates an identity-hiding mechanism based on zero-knowledge proof, enabling devices to authenticate and interact with resources anonymously during cross-domain operations, thereby safeguarding their privacy. Additionally, through threshold technology, the target domain can reveal the true identities of malicious devices and revoke their access rights, ensuring a balanced approach to security and privacy protection. The proposed scheme has been experimentally validated in a virtual environment and compared with existing solutions. Results demonstrate that DPCZK offers significant improvements in both effectiveness and efficiency.
To tackle emerging security and privacy concerns in consortium blockchain applications, particularly in handling sensitive identity information and transaction data, a novel privacy-preserving framework is proposed. The scheme leverages advanced technologies such as post-quantum cryptography, multi-party computation (MPC), and fully homomorphic encryption (FHE) to enhance security and efficiency. Firstly, a quantum-resistant blind signature mechanism is designed using lattice-based cryptography to mitigate centralization issues and strengthen security against quantum attacks. Secondly, a regulatory-compliant hierarchical encryption model is developed using fully homomorphic encryption, enabling fine-grained access control and data integrity verification without decrypting sensitive data. Additionally, zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) are integrated to ensure privacy while allowing verifiable computation. Experimental evaluations demonstrate that the proposed scheme achieves high encryption efficiency, reduced computational overhead during encryption/decryption, and strong resistance to malicious activities such as tampering, eavesdropping, and replay attacks
Jiahui Xiang, Osman Salem, Ahmed Meahoua, Santichai Wicha · 5 authors
Authentication is crucial in Identity and Access Management (IAM), especially within the Internet of Medical Things (IoMT), where security is critical. This paper proposes a solution to strengthen IoMT authentication, addressing its vulnerabilities and resource constraints. By leveraging blockchain as an Identity Provider (IdP) and integrating Zero-Knowledge Proof (ZKP) authentication, alongside Single Sign-On (SSO) standards, the proposed framework enhances security while simplifying the authentication process. Sharing the computational burden between the blockchain and client-side devices optimizes resource utilization, mitigating the strain on IoMT resources. Experimental results demonstrate improved RAM and computing power efficiency. This approach offers a robust solution to IoMT authentication challenges, promising heightened security and efficiency in healthcare IoT ecosystems. By fortifying authentication mechanisms, this framework contributes significantly to securing sensitive medical data and ensuring seamless access for authorized users in IoMT environments.
Paula Heess, Stefanie Holly, Marc-Fabian Körner, Astrid Nieße · 9 authors
Abstract The need to harness the flexibility of small-scale assets for system stabilization, including redispatch, is growing rapidly with the increasing prevalence of distributed generation, such as photovoltaic systems and heavy loads, in particular heat pumps and electric vehicles. Integrating these resources into the redispatch process presents special requirements: On the one hand, building trust with the owners of such assets requires privacy and a reasonable degree of autonomy and engagement. On the other hand, besides the system’s scalability and robustness, the verifiability and traceability of provided data are essential for grid operators who depend on the reliable provision of redispatch services. To date, research and practice have encountered significant challenges in defining a system that enables the inclusion of decentralized flexibilities while satisfying necessary requirements. To that end, we present a novel conceptual system design that addresses these challenges by combining a multi-agent system (MAS) approach with verifiable information flows through digital self-sovereign identities (SSIs) and Zero-Knowledge-Proofs (ZKPs). Single agents, as edge devices, operate locally and autonomously, respecting customer preferences, while MAS provide the ability to design robust, reliable, and scalable systems. SSI enables agents to manage their data autonomously, while ZKPs are used to protect users’ privacy through selective data disclosure which allows the verification of the correctness of information without disclosing the underlying data. To validate the feasibility of this design, a case study is included to demonstrate the functionality of key sub-processes, such as baseline optimization, aggregation, and disaggregation, in a realistic scenario. This case study, supported by a prototype implementation, provides initial evidence of the concept’s soundness and lays the groundwork for future evaluation through extensive simulations and field testing. Together, the technologies included in the conceptual system design balance full transparency for grid operators with autonomy and data economy for asset owners.
This research introduces FoReChain (Fog-based Re-Encryption Chain), a blockchain-enabled proxy re-encryption (PRE) framework designed for secure, low-latency data sharing in fog-based IoT networks. The framework addresses key challenges related to data security, privacy, and performance in distributed environments, where traditional models face issues like high latency, limited scalability, and inefficient key management. FoReChain integrates blockchain with ECC-based proxy re-encryption to secure data without exposing original content. A delegated Practical Byzantine Fault Tolerance (d-PBFT) consensus mechanism ensures efficient transaction validation. The framework processes data at fog nodes, reducing delays commonly found in cloud-dependent models. Key management relies on time-based key updates stored immutably on the blockchain, while zero-knowledge proofs support secure, anonymous data sharing. The study evaluates FoReChain against FE-PRE and PREA models using metrics such as latency, throughput, scalability, blockchain consensus time, and adaptive policy effectiveness. Results show lower latency, higher throughput, and better adaptability in FoReChain, especially under heavy network conditions like smart healthcare and industrial IoT setups. FoReChain demonstrates secure data sharing, efficient resource utilization, and reliable key management in dynamic IoT environments. It offers consistent performance under varying loads, with improved scalability and data integrity maintained through decentralized validation.
This comprehensive technical article explores the evolution and architectural landscape of Decentralized Finance (DeFi), examining its transformation from initial implementations to the more sophisticated DeFi 2.0 ecosystem. It investigates the fundamental technical components, including smart contract infrastructure, consensus mechanisms, and cross-chain interoperability solutions that form the backbone of modern DeFi systems. Through a detailed examination of Layer-2 scaling solutions, risk management protocols, and AI-driven analytics, the article highlights how DeFi aggregators are revolutionizing user interactions with decentralized protocols. It further delves into emerging technologies such as zero-knowledge proofs and quantum-resistant cryptography, while addressing critical challenges in security, scalability, and mainstream adoption. By examining both technical innovations and integration challenges, this article provides insights into how DeFi is reshaping traditional financial systems through decentralized protocols and smart contracts while emphasizing the importance of balancing innovation with security and regulatory compliance in the evolving blockchain landscape.