Sharon Rose H, Razeenath Aafiya A, Kamachi Swetha R, G. R. Ramya
The system focuses on proposing a robust authentication framework based on these Secure Remote Password (SRP) protocol to enhance the security of data exchange in Peer-to- Peer (P2P) cloud environments. The Advanced Encryption Standard (AES) algorithm provides encryption and the SRP protocol leverages cryptographic primitives to establish mutual authentication between cloud servers and users, ensures that data, remains protected against any vulnerabilities. The system encompasses various stages, including user registration, key establishment, and secure data transmission. After user registration, the SRP protocol employs a zero-knowledge proof mechanism to maintain the security of data available on cloud servers, mitigating the risks associated with password leaks and unauthorized access. Furthermore, the protocol facilitates secure key exchange to establish a confidential communication channel between peers, enabling encrypted data transmission. The system integrates SRP protocol and AES encryption, fortifying data security in P2P cloud environments through robust authentication, ensuring a comprehensive defence against potential vulnerabilities.
The rise of technology has brought with it a heightened awareness of the necessity to shield personal data and maintain exclusive access to specific knowledge. A notable solution that emerged from this consciousness is Zero-Knowledge Proofs (ZKPs) and, more specifically, Schnorr’s Protocol. Historically, Zero-Knowledge Proofs have a compelling lineage, tracing their roots back to the fervent discussions among cryptographers aiming to achieve a balance between information sharing and privacy. ZKPs are cryptographic methods that allow one party to prove to another that a statement is true, without revealing any specific information about the statement itself. In the midst of these developments, Schnorr’s Protocol emerged as a renowned interactive proof system. It possesses an intuitive structure that has made it pivotal in the enhancement of digital security. The typical flow of Schnorr’s Protocol begins with the prover sending a commitment to the verifier. The verifier then sends a random challenge back to the prover, who, in turn, produces a response. What’s captivating is that the verifier can ascertain the validity of the proof without gaining insight into the underlying secret. Interactive Schnorr’s Protocol involves real-time back-and-forth communication between the prover and verifier. On the other hand, the non-interactive version eliminates this need by using a cryptographic hash function, thereby streamlining the process.
A system of zero-knowledge proofs on graph signatures has been proposed, where a graph can be signed, and the owner of the graph signature can prove a graph relation such as the connectivity and isolation of any two vertexes on the graph without disclosing all information about the graph. The correctness of the graph information is guaranteed by the signature. One of the applications is a virtualized infrastructure, where an infrastructure provider manages a distributed system, and each tenant is allocated a specific portion of this infrastructure for use. Tenants need to check with the provider that their resources are properly connected (connectivity) and that their resources are properly separated from the resources of other tenants (isolation). On the other hand, the provider cannot simply disclose the entire infrastructure topology to each tenant. Using the zero-knowledge proof system on graph signatures, both requirements can be addressed. Previously, an efficient zero-knowledge proof system on graph signatures using a bilinear-map accumulator has been proposed, where the verification time and the size of the proof data do not depend on the number of graph vertexes and edges. However, this system has two problems. First, since the proof does not include labels, it is not possible to prove the connectivity considering network bandwidth and cost. Second, since it assumes undirected graphs, it cannot handle applications on directed graphs such as network flows. In this paper, we extend the previous system and propose a zero-knowledge proof system of the connectivity for directed graphs where each edge has labels. We implemented our system on a PC using a pairing library and evaluate it by measuring the processing times. Compared to the conference version of this paper, we show the formal definitions and the security proofs of our proposed system, and add implementation-based evaluations reflecting the application to the virtualized infrastructure.
Federated Learning (FL) emerges as a viable solution to facilitate data collaboration, enabling multiple clients to collaboratively train a machine learning (ML) model under the supervision of a central server while ensuring the confidentiality of their raw data. However, existing studies have unveiled two main risks: (i) the potential for the server to infer sensitive information from the client's uploaded updates (i.e., model gradients), compromising client input privacy, and (ii) the risk of malicious clients uploading malformed updates to poison the FL model, compromising input integrity. Recent works utilize secure aggregation with zero-knowledge proofs (ZKP) to guarantee input privacy and integrity in FL. Nevertheless, they suffer from extremely low efficiency and, thus, are impractical for real deployment. In this paper, we propose a novel and highly efficient approach RiseFL for secure and verifiable data collaboration, ensuring input privacy and integrity simultaneously. Firstly, we devise a probabilistic integrity check method that transforms strict checks into a hypothesis test problem, offering great optimization opportunities. Secondly, we introduce a hybrid commitment scheme to satisfy Byzantine robustness with improved performance. Thirdly, we present an optimized ZKP generation and verification technique that significantly reduces the ZKP cost based on probabilistic integrity checks. Furthermore, we theoretically prove the security guarantee of RiseFL and provide a cost analysis compared to state-of-the-art baselines. Extensive experiments on synthetic and real-world datasets suggest that our approach is effective and highly efficient in both client computation and communication. For instance, RiseFL is up to 28x, 53x, and 164x faster than baselines ACORN, RoFL, and EIFFeL for the client computation.
Firas Hamila, Mohammad Hamad, Daniel Costa Salgado, Sebastian Steinhorst
Abstract With the rapid expansion of IoT devices and their applications, there is an increasing demand for efficient and secure authentication mechanisms to protect against unauthorized access. Traditional authentication mechanisms face limitations regarding computational speed, communication costs, and vulnerability to cyber-attacks. Zero-knowledge proof (ZKP) protocols have emerged as an effective solution for achieving secure and efficient authentication in such environments without revealing sensitive information. Among ZKP protocols, $$\Sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Σ</mml:mi> </mml:math> -protocols, a class of interactive ZKP protocols, have been employed for their efficiency and security. However, their interactive nature necessitates multiple rounds of communication, which can reduce efficiency and increase communication overhead for resource-constrained devices. Many works have aimed to eliminate the interaction of $$\Sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Σ</mml:mi> </mml:math> -protocols by utilizing a transformation called the Fiat–Shamir transformation (FST). However, there is still a concern regarding the soundness of the FST as it can sometimes convert a secure $$\Sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Σ</mml:mi> </mml:math> -protocol into an insecure non-interactive zero-knowledge (NIZK) authentication scheme. In this paper, we propose an approach for transforming $$\Sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Σ</mml:mi> </mml:math> -protocols into a NIZK protocol based on the FST, yielding significant enhancements in efficiency, communication overhead reduction, and elimination of interaction. Our proposed protocol enables the completion of the authentication process in a single request while also strengthening the soundness of $$\Sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Σ</mml:mi> </mml:math> -protocols in comparison with the traditional FST by requiring two authentication factors instead of one. To demonstrate our approach’s robustness, we conducted comprehensive informal and formal security analyses (using the Tamarin-Prover). Our protocol demonstrated completeness, soundness, zero-knowledge properties, and robustness against attacks, including eavesdropping, message modification, replay, and brute force attacks. Additionally, our performance analysis displayed a remarkable 50% improvement in computational cost compared to traditional $$\Sigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Σ</mml:mi> </mml:math> -protocols, underscoring its efficiency for practical use.
A zeroknowledge proof is a game between a prover and a verifier, where the aim of the prover is to present the correctness of an assertion without giving the actual proof. This enables systems to achieve privacy with transparency and, if required, compliance. Zero knowledge proofs have been used in protection of data in healthcare and other systems, anonymous verifiable voting, financial systems like digital currencies, secure exchange of digital assets, secure remote biometric authentication, secure auction, and blockchain. The challenge of zero-knowledge proofs is that they are computationally hard to produce, some implementations need a trusted third party for setup and system initialization, some require efficiency optimization methods. Moreover, common algorithms cannot resist quantum computing attacks warranting research in the area.
In recent years, the increasing prevalence of digital transactions and data sharing has underscored the critical importance of privacy and security in the digital realm. Blockchain technology, initially designed to support cryptocurrencies like Bitcoin, has evolved into a versatile platform with the potential to provide trust, transparency, and data immutability in a wide range of applications. Concurrently, zero-knowledge proofs have emerged as a pioneering approach to safeguarding data privacy, allowing parties to validate information without disclosing the underlying data. This review paper thoroughly investigates the convergence of blockchain technology and zero-knowledge proofs, aiming to understand how their synergy can foster privacy-preserving transactions. We examine the foundational principles of zero-knowledge proofs, their theoretical underpinnings, and practical implementation within blockchain networks. Through a comprehensive analysis, we assess the effectiveness of this amalgamation in protecting sensitive data and consider its applicability to diverse use cases. This review provides a detailed exploration of the synergistic potential of blockchain and zero-knowledge proofs in reshaping the landscape of data privacy in an increasingly interconnected world.
The privacy concern in smart contract applications continues to grow, leading to the proposal of various schemes aimed at developing comprehensive and universally applicable privacy-preserving smart contract (PPSC) schemes. However, the existing research in this area is fragmented and lacks a comprehensive system overview. This paper aims to bridge the existing research gap on PPSC schemes by systematizing previous studies in this field. The primary focus is on two categories: PPSC schemes based on cryptographic tools like zero-knowledge proofs, as well as schemes based on trusted execution environments. In doing so, we aim to provide a condensed summary of the different approaches taken in constructing PPSC schemes. Additionally, we also offer a comparative analysis of these approaches, highlighting the similarities and differences between them. Furthermore, we shed light on the challenges that developers face when designing and implementing PPSC schemes. Finally, we delve into potential future directions for improving and advancing these schemes, discussing possible avenues for further research and development.
Alex Berke, Tobin South, Robert Mahari, Kent Larson · 5 authors
Tax returns contain key financial information of interest to third parties: public officials are asked to share financial data for transparency, companies seek to assess the financial status of business partners, and individuals need to prove their income to landlords or to receive benefits. Tax returns also contain sensitive data such that sharing them in their entirety undermines privacy. We introduce a zero-knowledge tax disclosure system (zkTax) that allows individuals and organizations to make provable claims about select information in their tax returns without revealing additional information, which can be independently verified by third parties. The system consists of three distinct services that can be distributed: a tax authority provides tax documents signed with a public key; a Redact & Prove Service enables users to produce a redacted version of the tax documents with a zero-knowledge proof attesting the provenance of the redacted data; a Verify Service enables anyone to verify the proof. We implement a prototype with a user interface, compatible with U.S. tax forms, and demonstrate how this design could be implemented with minimal changes to existing tax infrastructure. Our system is designed to be extensible to other contexts and jurisdictions. This work provides a practical example of how distributed tools leveraging cryptography can enhance existing government or financial infrastructures, providing immediate transparency alongside privacy without system overhauls.
John Bostanci, Luowen Qian, Nicholas Spooner, Henry Yuen
We prove a tight parallel repetition theorem for $3$-message computationally-secure quantum interactive protocols between an efficient challenger and an efficient adversary. We also prove under plausible assumptions that the security of $4$-message computationally secure protocols does not generally decrease under parallel repetition. These mirror the classical results of Bellare, Impagliazzo, and Naor [BIN97]. Finally, we prove that all quantum argument systems can be generically compiled to an equivalent $3$-message argument system, mirroring the transformation for quantum proof systems [KW00, KKMV07]. As immediate applications, we show how to derive hardness amplification theorems for quantum bit commitment schemes (answering a question of Yan [Yan22]), EFI pairs (answering a question of Brakerski, Canetti, and Qian [BCQ23]), public-key quantum money schemes (answering a question of Aaronson and Christiano [AC13]), and quantum zero-knowledge argument systems. We also derive an XOR lemma [Yao82] for quantum predicates as a corollary.
Federated learning, as one of the three main technical routes for privacy computing, has been widely studied and applied in both academia and industry. However, malicious nodes may tamper with the algorithm execution process or submit false learning results, which directly affects the performance of federated learning. In addition, learning nodes can easily obtain the global model. In practical applications, we would like to obtain the federated learning results only by the demand side. Unfortunately, no discussion on protecting the privacy of the global model is found in the existing research. As emerging cryptographic tools, the zero-knowledge virtual machine (ZKVM) and homomorphic encryption provide new ideas for the design of federated learning frameworks. We have introduced ZKVM for the first time, creating learning nodes as local computing provers. This provides execution integrity proofs for multi-class machine learning algorithms. Meanwhile, we discuss how to generate verifiable proofs for large-scale machine learning tasks under resource constraints. In addition, we implement the fully homomorphic encryption (FHE) scheme in ZKVM. We encrypt the model weights so that the federated learning nodes always collaborate in the ciphertext space. The real results can be obtained only after the demand side decrypts them using the private key. The innovativeness of this paper is demonstrated in the following aspects: 1. We introduce the ZKVM for the first time, which achieves zero-knowledge proofs (ZKP) for machine learning tasks with multiple classes and arbitrary scales. 2. We encrypt the global model, which protects the model privacy during local computation and transmission. 3. We propose and implement a new federated learning framework. We measure the verification costs under different federated learning rounds on the IRIS dataset. Despite the impact of homomorphic encryption on computational accuracy, the framework proposed in this paper achieves a satisfactory 90% model accuracy. Our framework is highly secure and is expected to further improve the overall efficiency as cryptographic tools continue to evolve.
Sylvain Chatel, Christian Mouchet, Ali Utkan Şahin, Apostolos Pyrgelis · 6 authors
Multiparty fully homomorphic encryption (MFHE) schemes enable multiple parties to efficiently compute functions on their sensitive data while retaining confidentiality. However, existing MFHE schemes guarantee data confidentiality and the correctness of the computation result only against honest-but-curious adversaries. In this work, we provide the first practical construction that enables the verification of MFHE operations in zero-knowledge, protecting MFHE from malicious adversaries. Our solution relies on a combination of lattice-based commitment schemes and proof systems which we adapt to support both modern FHE schemes and their implementation optimizations. We implement our construction in PELTA. Our experimental evaluation shows that PELTA is one to two orders of magnitude faster than existing techniques in the literature.
Yibin Yang, David Heath, Carmit Hazay, Vladimir Kolesnikov · 5 authors
Vector Oblivious Linear Evaluation (VOLE) supports fast and scalable interactive Zero-Knowledge (ZK) proofs. Despite recent improvements to VOLE-based ZK, compiling proof statements to a control-flow oblivious form (e.g., a circuit) continues to lead to expensive proofs. One useful setting where this inefficiency stands out is when the statement is a disjunction of clauses \mathcalL _1 Å‚or \cdots Å‚or \mathcalL _B. Typically, ZK requires paying the price to handle all B branches. Prior works have shown how to avoid this price in communication, but not in computation.
Payment channel hubs (PCHs) serve as a promising solution to achieving quick off-chain payments between pairs of users. They work by using an untrusted tumbler to relay the payments between the payer and payee and enjoy the advantages of low cost and high scalability. However, the most recent privacy-preserving payment channel hub solution that supports variable payment amounts suffers from limited unlinkability, e.g., being vulnerable to the abort attack. Moreover, this solution utilizes zero-knowledge proofs, which bring huge costs on both computation time and communication overhead. Therefore, how to design PCHs that support variable amount payments and unlinkability, but reduce the use of huge-cost cryptographic tools as much as possible, is significant for the large-scale practical applications of off-chain payments.
Open access
Blockchain Technology Applications and Security
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
Despite the notable advances in the development of high-assurance, verified implementations of cryptographic protocols, such implementations typically face significant performance overheads, particularly due to the penalties induced by formal verification and automated extraction of executable code. In this paper, we address some core performance challenges facing computer-aided cryptography by presenting a formal treatment for accelerating such verified implementations based on multiple generic optimizations covering parallelism and memory access. We illustrate our techniques for addressing such performance bottlenecks using the Line-Point Zero-Knowledge (LPZK) protocol as a case study. Our starting point is a new verified implementation of LPZK that we formalize and synthesize using EasyCrypt; our first implementation is developed to reduce the proof effort and without considering the performance of the extracted executable code. We then show how such (automatically) extracted code can be optimized in three different ways to obtain a 3000x speedup and thus matching the performance of the manual implementation of LPZK of lpzkv2.[13] We obtain such performance gains by first modifying the algorithmic specifications, then by adopting a provably secure parallel execution model, and finally by optimizing the memory access structures. All optimizations are first formally verified inside EasyCrypt, and then executable code is automatically synthesized from each step of the formalization. For each optimization, we analyze performance gains resulting from it and also address challenges facing the computer-aided security proofs thereof, and challenges facing automated synthesis of executable code with such an optimization.
Yuyang Sang, Ning Luo, Samuel Judson, Ben Chaimberg · 8 authors
A zero-knowledge proof (ZKP) is a powerful cryptographic primitive used in many decentralized or privacy-focused applications. However, the high overhead of ZKPs can restrict their practical applicability. We design a programming language, Ou, aimed at easing the programmer's burden when writing efficient ZKPs, and a compiler framework, Lian, that automates the analysis and distribution of statements to a computing cluster. Ou uses programming language semantics, formal methods, and combinatorial optimization to automatically partition an Ou program into efficiently sized chunks for parallel ZK-proving and/or verification. We contribute: (1) A front-end language where users can write proof statements as imperative programs in a familiar syntax; (2) A compiler architecture and implementation that automatically analyzes the program and compiles it into an optimized IR that can be lifted to a variety of ZKP constructions; and (3) A cutting algorithm, based on Pseudo-Boolean optimization and Integer Linear Programming, that reorders instructions and then partitions the program into efficiently sized chunks for parallel evaluation and efficient state reconciliation.
How can a model owner prove they trained their model according to the correct specification? More importantly, how can they do so while preserving the privacy of the underlying dataset and the final model? We study this problem and formulate the notion of zero-knowledge proof of training (zkPoT), which formalizes rigorous security guarantees that should be achieved by a privacy-preserving proof of training. While it is theoretically possible to design zkPoT for any model using generic zero-knowledge proof systems, this approach results in extremely unpractical proof generation times. Towards designing a practical solution, we propose the idea of combining techniques from MPC-in-the-head and zkSNARKs literature to strike an appropriate trade-off between proof size and proof computation time. We instantiate this idea and propose a concretely efficient, novel zkPoT protocol for logistic regression.
Privacy is one of the essential pillars for the widespread adoption of blockchains, but public blockchains are transparent by nature. Modern analytics techniques can easily subdue the pseudonymity feature of a blockchain user. Some applications have been able to provide practical privacy protections using privacy-preserving cryptography techniques. However, malicious actors have abused them illicitly, discouraging honest actors from using privacy-preserving applications as "mixing" user interactions and funds with anonymous bad actors, causing compliance and regulatory concerns. In this paper, we propose a framework that balances privacy-preserving features by establishing a regulatory and compliant framework called Selective De-Anonymization (SeDe). The adoption of this framework allows privacy-preserving applications on blockchains to de-anonymize illicit transactions by recursive traversal of subgraphs of linked transactions. Our technique achieves this without leaving de-anonymization decisions or control in the hands of a single entity but distributing it among multiple entities while holding them accountable for their respective actions. To instantiate, our framework uses threshold encryption schemes and Zero-Knowledge Proofs (ZKPs).
Zero-knowledge circuits are sets of equality constraints over arithmetic expressions interpreted in a prime field; they are used to encode computations in cryptographic zero-knowledge proofs. We make the following contributions to the problem of ensuring that a circuit correctly encodes a computation: a formal framework for circuit correctness; an ACL2 library for prime fields; an ACL2 model of the existing R1CS (Rank-1 Constraint Systems) formalism to represent circuits, along with ACL2 and Axe tools to verify circuits of this form; a novel PFCS (Prime Field Constraint Systems) formalism to represent hierarchically structured circuits, along with an ACL2 model of it and ACL2 tools to verify circuits of this form in a compositional and scalable way; verification of circuits, ranging from simple to complex; and discovery of bugs and optimizations in existing zero-knowledge systems.
Open access
2 source records
Physical Unclonable Functions (PUFs) and Hardware Security
Decentralized physical infrastructure network (DePIN) is an emerging research topic in Web3 and blockchain. By combining blockchain, IoT and tokenomics, DePINs are expected to disrupt existing IoT business models and enable Web3 communities to build innovative, machine-driven and decentralized IoT networks and applications. Due to the characteristics of DePINs such as the large number of smart devices and network scale as well as the interactions with blockchain, scalability remains to be one of the key challenges. In this position paper, we outline the core ideas and components for building a rollup-centric scalable architecture for DePINs. The proposed architecture takes a modular design approach and leverages off-chain computing and zero-knowledge proofs to address the scalability challenge. This work is expected to highlight the importance of this new research direction and shed some light on potential solutions.
This paper proposes a novel recursive polynomial commitment scheme (PCS) and a new polynomial interactive oracle proof (PIOP) protocol, which compile into efficient and transparent zk-SNARKs (zero-knowledge succinct non-interactive arguments of knowledge). The Ethereum blockchain utilizes zero-knowledge Rollups (ZKR) to improve its scalability (the ability to handle a large number of transactions), and ZKR uses zk-SNARKs to validate transactions. The currently used zk-SNARKs rely on a trusted setup ceremony, where a group of participants uses secret information about transactions to generate the public parameters necessary to verify the zk-SNARKs. This introduces a security risk into Ethereum's system. Thus, researchers have been developing transparent zk-SNARKs (which do not require a trusted setup), but those are not as efficient as non-transparent zk-SNARKs, so ZKRs do not use them. In this research, I developed LUMEN, a set of novel algorithms that generate transparent zk-SNARKs that improve Ethereum's security without sacrificing its efficiency. Various techniques were creatively incorporated into LUMEN, including groups with hidden orders, Lagrange basis polynomials, and an amortization strategy. I wrote mathematical proofs for LUMEN that convey its completeness, soundness and zero-knowledgeness, and implemented LUMEN by writing around $8000$ lines of Rust and Python code, which conveyed the practicality of LUMEN. Moreover, my implementation revealed the efficiency of LUMEN (measured in proof size, proof computation time, and verification time), which surpasses the efficiency of existing transparent zk-SNARKs and is on par with that of non-transparent zk-SNARKs. Therefore, LUMEN is a promising solution to improve Ethereum's security while maintaining its efficiency.
Abstract Providing trust in machine learning (ML) systems and their fairness is a socio-technical challenge, and while the use of ML continues to rise, there is lack of adequate processes and governance practices to assure their fairness. In this paper, we propose FaaS, a novel privacy-preserving, end-to-end verifiable solution, that audits the algorithmic fairness of ML systems. FaaS offers several features, which are absent from previous designs. The FAAS protocol is model-agnostic and independent of specific fairness metrics and can be utilised as a service by multiple stakeholders. FAAS uses zero knowledge proofs to assure the well-formedness of the cryptograms and provenance in the steps of the protocol. We implement a proof of concept of the FaaS architecture and protocol using off-the-shelf hardware, software, and datasets and run experiments to demonstrate its practical feasibility and to analyse its performance and scalability. Our experiments confirm that our proposed protocol is scalable to large-scale auditing scenarios (e.g. over 1000 participants) and secure against various attack vectors.
Md Jahangir Alam, Ismail Hossain, Sai Puppala, Sajedul Talukder
Identity attacks, such as impersonation, identity theft, and fraudulent account creation, pose significant threats to the security and trustworthiness of Online Social Networks (OSNs). In this paper, we propose a robust and secure framework to verify user identities without compromising their privacy by developing a multi-layered framework leveraging zero-knowledge proof (ZKP) and Hyperledger Fabric private blockchain. We introduce a blockchain-based government identity provider system, coupled with a zero-knowledge proof-based signup process for social networks. Our prototype authenticates user identities in multiple layers, effectively mitigating fraudulent, cloned, and multiple account creations. Our experiments with n (n = 50) users showed a 100% success rate for our system, highlighting its effectiveness compared to other OSNs.
Blockchain based E-voting system can be revolution- ary in terms of security and tamper proof, transparent elections and hence empower the democracy. Blockchain E-voting is already implemented at organisational level. But at state/national level it faces challenges such as privacy, stability, latency etc. To counter them we require advanced implementation techniques from multiple disciplines. These techniques are 2-layer-solution, sharding for scalability issue, optimizing consensus algorithm and off-chain transaction for reducing latency, zero knowledge proof, using multiple protocols for user privacy etc. General architecture of blockchain is explained in details which combines election creation, voter registration, voter transaction, tallying the results and vote verification. We have discussed how integrating E-voting with blockchain document verification system can be benificial. It is complicated to implement at state, national level and need additional research and implementation to make blockchain E- voting acceptable in elections across the world. Through research and innovation we can make blockchain voting mainstream.
Open access
Internet Traffic Analysis and Secure E-voting
Network Traffic and Congestion Control
Advanced Steganography and Watermarking Techniques