Cross-border candidate credential verification using ZKP and blockchain Ethereum and Polygon perspectives: a scalable solution for authentic global corporate interviews

In the contemporary global job market, the secure and efficient verification of a candidate’s academic qualifications presents a significant challenge, particularly across international boundaries. Conventional techniques frequently necessitate physical documents or PDF scans, rendering them inefficient, susceptible to falsification, and hazardous about privacy. This study presents a contemporary, scalable framework that integrates Zero-Knowledge Proofs (ZKPs), blockchain technology, and decentralized storage (IPFS) to establish a secure, privacy-oriented method for candidate verification. In this proposed system, candidates submit their academic documents, which are digitally signed by the issuing universities using cryptographic methods. The signed files are preserved on IPFS, guaranteeing their integrity and accessibility. The hash of each document is then stored on a blockchain, either Ethereum or Polygon, offering a public and immutable reference. Zero-Knowledge Proofs enable candidates to validate the legitimacy of their credentials while safeguarding sensitive information. Human Resources teams can authenticate these documents in real time, validating their integrity against the blockchain hash while preserving the candidate’s confidentiality. The evaluation results demonstrate that Ethereum offers robust decentralization and trust; nevertheless, Polygon proved to be more pragmatic because to its reduced gas price and expedited transaction times, making it suitable for high-volume recruitment. This proposed initiative addresses weaknesses in digital recruitment by guaranteeing trust, privacy, and automated credential verification procedure. It provides a customized approach for present recruitment requirements, particularly for organizations engaged in cross-border hiring, where security, scalability and protection of candidate information are paramount.

1 Introduction

In today’s fast paced and globally connected job market, organizations increasingly rely on digital tools to streamline their hiring processes. As remote work and international recruitment become more common, verifying the authenticity of a candidate’s qualifications has never been more critical. Yet, the systems currently in place often fall short of the transparency, efficiency, and security that modern employers and candidates expect. This raises important questions about how we can build trust without compromising privacy. The verification of academic credentials plays a pivotal role in modern recruitment processes, ensuring the legitimacy of a candidate’s qualifications. However, conventional methods require candidates to submit physical or digital copies of degree certificates and mark sheets directly to employers (Chakrabarti and Chauhan, 2018). This approach not only risks exposure of personal and sensitive information but also opens the door to potential misuse, identity theft, and document forgery. Furthermore, such centralized verification systems often suffer from inefficiencies, including prolonged processing times, human errors, and difficulties in cross-border validations, which make them unsustainable in the evolving digital economy (Xue et al., 2019). These shortcomings underscore the pressing need for a secure, efficient, and privacy-preserving mechanism for candidate verification.

Recent advancements in cryptography and distributed ledger technologies (DLTs) have emerged as viable solutions to these long-standing challenges. One particularly promising cryptographic tool is the Zero-Knowledge Proof (ZKP), which allows a person to prove they possess certain knowledge (such as a valid academic certificate) without revealing the actual document or any underlying data (Goldwasser et al., 1989; Chaum and Pedersen, 1993; Bhasker Reddy and Duvvi, 2024). This is a transformative approach, especially in candidate verification, where privacy preservation is paramount. By eliminating the need to share raw credentials, ZKPs drastically reduce the risk of data leaks, unauthorized access, and identity theft. Despite these advantages, ZKPs are not without limitations. While they enable privacy-preserving verification, they do not inherently validate the authenticity of the original document from which the proof is generated. In other words, a ZKP can confirm that a document exists and matches a known structure, but it cannot verify who issued the document or whether it is genuine. This opens the door to a critical loophole an attacker could forge a fake academic certificate, generate a legitimate looking ZKP from it, and still pass verification without ever exposing the fraud (Zhang et al., 2017).

To address this vulnerability, it is essential to combine ZKPs with tamper-proof and decentralized data anchoring mechanisms, such as blockchain. A viable solution involves universities digitally signing the credentials they issue and recording their cryptographic hashes on a public blockchain. This immutable record acts as a trust anchor, enabling any verifier to confirm that a ZKP corresponds to a document that was indeed issued by a trusted institution (Alammary et al., 2019). Integrating these technologies offers a compelling path forward: candidates can generate ZKPs from cryptographically signed credentials stored in IPFS, while verifiers can check the proof against hash records on the blockchain without ever accessing the private contents of the credentials themselves. This end-to-end pipeline supports scalable, privacy-respecting, and fraud-resistant candidate verification, ideally suited for cross-border digital recruitment. Eventually, this approach not only enhances security and privacy but also builds trust in the hiring process benefiting employers, institutions, and candidates alike in an increasingly decentralized and data-conscious world.

To address the limitations of traditional credential verification systems, we propose a secure and decentralized framework that integrates Zero-Knowledge Proofs (ZKPs), blockchain technology, and the InterPlanetary File System (IPFS) for distributed storage. In this system, academic institutions play a pivotal role by digitally signing the credentials they issue. Candidates request their universities to generate cryptographic signatures for their certificates, ensuring authenticity at the source. These signed documents are then uploaded to IPFS using the Pinata gateway, enabling decentralized, tamper-proof, and persistent storage. To maintain a publicly verifiable reference, the document’s cryptographic hash is concurrently stored on a public blockchain, forming an immutable link between the candidate’s proof and the original, institution-issued data. When applying for a position, the candidate does not submit their actual certificate. Instead, a Zero-Knowledge Proof is generated from the IPFS-stored signed credential. This proof allows Human Resource (HR) professionals to verify that the certificate is both authentic and unaltered by simply comparing the hash embedded in the proof against the one anchored on the blockchain without accessing or exposing the content of the document. This mechanism drastically reduces privacy risks while increasing the confidence and speed of the verification process.

1.1 Problem definition

In the contemporary employment market verifying an applicant’s academic and professional qualifications across borders is still fundamental yet extremely difficult. Credentials verification options still rely on centralized systems and manual verification of documents that are often laborious, expensive and subject to fraud, forgery and privacy breaches. All too often applicants must furnish sensitive information like degree certificates and or transcripts directly to employers increasing chances of identity theft and improper use of data. Credentials verification options commonly do not support interoperability between institutions and recruiters causing inefficiencies in the digital hiring process. Therefore, there is an crucial need for a secure decentralized and privacy preserving verification model that allows for verification of the authenticity of documents while protecting candidate data.

1.2 Motivation

The impetus for the current work is motivated by the existing limitations of credential authorization processes and the increasing need for trustless, cross-border digital hiring solutions. New technologies such as blockchain, and more recently, Zero-Knowledge Proofs (ZKPs), represent an innovative way to achieve this goal. Blockchain offers data immutability and transparency, while ZKPs allow candidates to verify their credentials or certification without sharing that information. When these technologies are integrated with decentralized storage, such as IPFS, they can create a verification scheme that is efficient, scalable, and privacy compliant a baseline necessity for modern data protection and global mobility.

The aim of this research is to enhance privacy in candidate verification through the application of ZKPs, while simultaneously guaranteeing the authenticity of credentials via university-issued digital signatures. By employing blockchain as a trustless and tamper-resistant ledger to store document hashes, the system ensures that records remain verifiable and immutable. Furthermore, the process streamlines hiring for HR professionals by automating trust, reducing manual effort, and minimizing human error. To evaluate real-world feasibility, we also compare the performance, cost-efficiency, and scalability of leading blockchain platforms, specifically Ethereum and Polygon as infrastructures for implementing the framework. Ultimately, this research contributes to the development of a privacy-first, scalable, and globally interoperable model for digital hiring in an increasingly connected workforce.

1.3 Research contributions and novelty of the proposed work

• The contribution of this work involves a new domain-specific combination of zk-SNARK based verification, decentralized storage via IPFS and multi-platform blockchain deployment with Ethereum and Polygon.

• In introducing a trustless verification pipeline that allows universities to cryptographically sign academic credentials, candidates to create zero-knowledge proofs from the IPFS links, and HR verifiers the ability to verify at attestation. This system looks past the need for candidates to provide personally identifiable information and allows verification without access to sensitive data.

• This system also implements a gas-optimized smart contract design, a Merkle root to reduce the IPFS storage of credentials and zero knowledge proof, which solidify its contributions to existing Blockchain identity systems and their outstanding systems.

• This framework demonstrates a new reliability for secure, scalable, and privacy focused recruitment processes in the global context.

Existing literature has discussed Blockchain and ZKPs in various settings, ranging from digital identity management to e-voting, yet they have not yet been combined to address the issue of a cross-border verification of candidate credentials where privacy, authenticity and scalability must co-exist.

2 Literature survey

Credential verification in recruitment has traditionally relied on centralized authorities and manual checks, which are prone to delays, fraud, and privacy concerns. Recent research has explored the integration of blockchain, decentralized storage, and zero-knowledge proofs to build secure, tamper-proof, and privacy-preserving verification frameworks. Trusted distributed educational records bring merits such as secure and tamper-proof credentials, universal access across institutions, greater transparency, broader recognition of achievement, and more open participation in knowledge sharing. Yet, there are serious demerits: privacy risks in storing personal data on public ledgers, difficulty in valuing ideas or academic work, the danger of commodifying education, possible widening of inequalities, and overreliance on reputation scores at the cost of genuine learning. Blockchain, the foundation of the Bitcoin digital payment system, integrates several interrelated technologies: a distributed ledger of digital events, consensus mechanisms to validate new blocks, automated smart contracts, and the data structures within each block. Building on this model, authors in (Sharples and Domingue, 2016) propose a permanent, distributed record of intellectual contributions and corresponding reputational rewards, using blockchain to extend and democratize educational reputation beyond traditional academia. To explore this vision, experiments were conducted using private blockchain for storing educational records, informed by our earlier research on reputation management in educational contexts.

Study presented by (El Koshiry et al., 2023) uses merits of blockchain’s potential to create tamper-proof academic transcripts and streamline credential verification for students and employers, while also acknowledging the challenges of adoption and regulation. Despite the ensured merits like transparency, streamlined credential verfications and improved on line learning, Widespread adoption and the ability to handle large-scale implementations remain significant challenges. Blockchain, combined with digital signatures via ECDSA, offers secure, transparent, and verifiable credential management, empowering learners to share proofs directly with employers is demonstrated by (Al Hemairy et al., 2024) However, challenges remain in safeguarding privacy, managing cryptographic keys, and ensuring wide institutional adoption. Addressing these issues is crucial to realizing a trusted, tamper-proof digital credential ecosystem.

The study conducted by (Sree and Sree, 2025) emphasizes a decentralized system for granting certificates and verification, constructed on the Ethereum blockchain and evaluated on the Goerli testnet. The concept guarantees tamper-proof and transparent credential validation across various sectors including education, healthcare and government, hence minimizing dependence on centralized authorities and mitigating fraud threats. The approach enhances efficiency, scalability, and confidence by storing certificate data in smart contracts and facilitating automated verification. A user-centric mobile application improves accessibility, providing a viable route to safe and transparent digital credential systems. Still, several drawbacks remain such as elevated transaction costs and scalability constraints on public blockchains, the potential for privacy violations if sensitive information is improperly managed, dependent on user digital literacy, infrastructure and regulatory ambiguities that may limit broad use.

The InterPlanetary File System (IPFS) is a decentralized, peer-to-peer protocol intended for the storage and sharing of files, sometimes utilized in conjunction with blockchain to resolve issues related to data permanence and accessibility. Rather than utilizing conventional location-based storage, it adopts content addressing, wherein each file is designated by a unique hash, rendering it tamper-resistant and readily accessible. When combined with blockchain technology, these content hashes are documented on-chain, offering immutable and provable references to material held inside the distributed network.

This study, presented by (Rahman et al., 2023), illustrates that blockchain and IPFS can resolve the ongoing issue of counterfeit academic diplomas in Southeast Asia by establishing tamper-proof and immutable digital credentials. The technology produces a distinct hash for each certificate, safely kept on blockchain nodes, allowing businesses to efficiently and economically validate applicant information while minimizing storage costs using IPFS. Nonetheless, obstacles persist, including potential security vulnerabilities in interim database storage, the necessity for robust governance and institutional acceptance, privacy concerns about sensitive student information, inconsistent access to technological infrastructure, and potential scaling hurdles as usage increases.

The transparency of blockchain transactions supports trust and accountability, yet it also raises privacy issues, particularly in sectors such as finance, identity management and healthcare, where sensitive information must be kept confidential. Conventional blockchains disclose all transaction details to attain consensus and avert double-spending, resulting in a conflict between transparency and privacy. Zero-knowledge proofs (ZKPs) provide a mechanism for verifying transactions and smart contracts while concealing the underlying information. In addition to enhancing privacy, zero-knowledge proofs (ZKPs) augment scalability, as methodologies such as zk-SNARKs and zk-STARKs diminish proof size and verification expenses, thereby rendering blockchain systems more effective and viable.

Authors in (Zhang et al., 2017) propose zkLedger, a blockchain-based solution that allows banks to preserve privacy in digital asset transactions while facilitating rapid and reliable auditing. It utilizes Schnorr-type noninteractive zero-knowledge proofs, eliminating the necessity for trusted setup as required by zk-SNARKs and depending just on conventional cryptographic assumptions. The columnar ledger architecture assures completeness by eliminating concealed transactions and enables auditors to verify balances with cryptographic assurances of accuracy, delivering replies on ledgers containing up to 100,000 transactions in less than 10 milliseconds. Notwithstanding these advantages, zkLedger encounters difficulties with scalability with extensive real-world datasets, connection with current financial systems, and regulatory acceptance. Moreover, its dependence on exact cryptographic execution and specialized technical knowledge may constrain its practical application.

Zero-Knowledge Proofs (ZKPs) enable a prover to show awareness of a secret while keeping it confidential. Designated Verifier Proofs (DVPs) enhance this concept by limiting verification to authorized entities however, they become ineffective if proof-related information is exposed on the blockchain, allowing for third-party validation. To tackle this issue, the authors in (Chi et al., 2023) present Blockchain Designated Verifier Proofs (BDVPs), enabling verifiers to create simulated proofs, thereby ensuring that outsiders cannot determine whether the proved genuinely possesses the secret. This maintains the confidentiality of the individual, in accordance with regulatory standards. The investigation delves into the risks associated with quantum attacks by introducing a post-quantum BDVP scheme and assesses its efficacy in comparison to current methodologies.

The investigation conducted by the authors (Wan et al., 2023) tackles the issue of maintaining both authenticity and privacy when blockchain-based smart contracts necessitate sensitive off-chain data. The authors introduce zk-DASNARK, an advanced extension of zk-SNARKs that integrates data authentication, and develop zk-AuthFeed, a zero-knowledge data feed scheme that facilitates secure and private inputs for decentralised applications. By employing a model that computes off-chain and verifies on-chain, zk-AuthFeed effectively alleviates the workload of validators while ensuring the privacy of users is safeguarded. A prototype evaluated on a medical insurance DApp shows impressive results, achieving key generation in approximately 10 s, proof generation in less than 4 s, and verification in under 40 ms, highlighting the scheme’s practicality and efficiency. Although zk-AuthFeed demonstrates notable efficiency, there are still hurdles to overcome in scaling the protocol for more intricate applications and addressing the challenges associated with trusted setup and its integration into current blockchain ecosystems.

To reduce the computation cost incurred in ZKP installation, authors in (Seo et al., 2025) presents a blockchain-based E-participation framework that enhances transparency and security while addressing key challenges in ZKP-based systems. It introduces attribute keys for efficient and privacy-preserving participant sampling and applies Shapley Values to design a fair differential reward system that incentivizes sincerity. A prototype with real participants validates its practicality and effectiveness. Unique challenges include scalability of Shapley Value computations, balancing privacy with accountability, and ensuring adoption in large-scale, real-world governance systems.

A blockchain-based Privacy-Preserving Identity Management (PPIdM) system that hides users’ real identities from all entities, including the identity provider, while still enabling identity traceability for malicious behavior is demonstrated in (Luong and Park, 2023). The presented study uniquely combines zk-SNARKs for anonymous authentication, Shamir’s Secret Sharing for identity recovery, and commitment schemes for selective attribute disclosure. The system ensures full anonymity in user activities and service history, while maintaining accountability through blockchain consensus. Performance evaluation shows the scheme is efficient in both on-chain and off-chain settings. However, drawbacks include the computational overhead of zk-SNARKs, potential scalability issues, reliance on complex cryptographic primitives, and challenges in practical deployment across diverse platforms.

An experimental study explores a privacy-preserving multi-factor authentication (MFA) system that combines blockchain technology and zero-knowledge proofs (ZKPs) to enhance security within a zero-trust framework, as detailed in (Diaz Rivera et al., 2024). The system implements a Distributed Authentication Mechanism (DAM), substituting centralized servers with a decentralized network of authenticators to minimize single points of failure. Blockchain-enabled zero-knowledge proofs facilitate the private verification of one-time passwords, guaranteeing both confidentiality and authenticity. During the concluding MFA phase, non-transferable NFTs serve as authentication tokens, flexible enough to align with different business logics. Experimental results showcase robust security and reliability, while forthcoming efforts will investigate the integration of intelligent system and open-source zero-trust platforms such as OpenZiti.

The design of deterministic smart contracts improves system operations. This study outlined in (Li et al., 2025) introduces a blockchain-based framework designed for the secure and efficient sharing of medical data assets (MDAs), featuring three primary innovations. The proposal outlines a structured architecture that distinctly separates tasks aimed at preserving privacy, while also facilitating incentives and enabling parallel execution. A supervisory mechanism has been established by integrating zero-knowledge proofs with group signatures within smart contracts, thereby ensuring privacy in decentralized settings. Furthermore, a model for incentives has been developed to equitably compensate participants and promote the sharing of data. Experimental results show robust privacy protection with a mere 2.2% decrease in throughput, while the parallelized ZKP execution underscores the framework’s scalability for extensive applications.

The analysis of the integration of machine learning with ZKP protocols was carried out in (Tran et al., 2022). An advanced AI-based biometric authentication framework that combines machine learning with zero-knowledge proofs (ZKPs) to improve privacy measures. Biometric data is transformed into binary strings, which are then categorized using Support Vector Machines (SVM) and Multi-Layer Perceptron (MLP) for the purposes of fingerprint and iris recognition. A new strategy for Composite Features Retrieval is introduced to enhance classifier accuracy, thereby increasing the discriminative power of binary strings. The verified outputs undergo hashing and are utilized within a zero-knowledge proof protocol, guaranteeing a privacy-preserving method of verification that does not reveal biometric information. Assessments conducted on various fingerprint and iris datasets reveal encouraging accuracy levels, with upcoming efforts aimed at enhancing security measures against model inversion, bitstring leakage, and investigating more data-efficient machine learning models.

A framework for Self-Sovereign Identity (SSI) utilizing two Zero-Knowledge Proof (ZKP) protocols grounded in the hardness of discrete logarithms is presented in (Dieye et al., 2023). Using the properties of group automorphisms, the system effectively connects various identities, identifiers, and attributes while avoiding the reuse of the same secret key, thereby addressing a significant limitation of the Schnorr protocol. This method guarantees limited sharing with a reliable third party and complete confidentiality from service providers, thereby improving privacy. The solution adheres completely to eIDAS, GDPR, and SSI design principles, guaranteeing alignment with regulatory standards. A novel ZKP protocol is introduced to enhance provability and securely integrate multiple SSIs within a single framework.

The application of ZKP in the Internet of Vehicles is examined in (Xi et al., 2022). A highly effective anonymous authentication method for the Internet of Vehicles (IoV) utilizing Zero-Knowledge Proofs (ZKP) and Elliptic Curve Cryptography (ECC). This approach guarantees robust anonymity, authenticity, and unlinkability, while also facilitating traceability via a trusted authority with the use of verification keys. A rapid reconnection mechanism is implemented to minimize computational overhead during frequent access. The security analysis validates the system’s resilience to replay attacks and its provision for forward security. The experimental findings demonstrate enhanced performance in comparison to traditional IoV authentication protocols.

An experiment presented in (Li et al., 2023) introduces the first aggregated Zero-Knowledge Proof (ZKP) and blockchain-based system for privacy-preserving identity verification in truck platooning. It ensures secure and efficient platoon formation without exposing sensitive vehicular or business data. The blockchain serves as a verifier, guarantees data immutability, and enforces programmable access control over platoon records. Experiments on Hyperledger show low latency, high throughput, and constant 500 ms verification time regardless of proof volume. The redesigned platooning process achieves fast and secure formation, proving feasibility for real-world deployment. An anonymous authentication scheme for IoV (Jiang and Guo, 2025) using a trustworthy roadside unit group (TRUG)-PBFT consensus and lattice-based ZKP. The improved TRUG-PBFT optimizes node grouping and main node selection with VRFs, boosting consensus efficiency. Vehicle data is securely managed across main and secondary chains to enhance privacy. Experiments show a 33% efficiency gain and 7.08 ms authentication cost, outperforming existing methods. AnoPay an anonymous parking payment scheme that ensures user anonymity with constant overhead using updatable attribute-based anonymous credentials and efficient ZKP is demonstrated in (Yang et al., 2024). A secure fee aggregation protocol with homomorphic encryption hides individual payments while protecting parking lot revenue privacy. The scheme achieves both unlinkability and accountability, enabling tracing of malicious users when required. Security proofs and experiments confirm its efficiency and practicality over existing methods.

A novel selective disclosure approach combining BLS signatures, Merkle Trees, and Bulletproofs (Ramić et al., 2024) to enable privacy-preserving digital credentials is demonstrated in (Ramić et al., 2024). It supports selective claim disclosure while meeting defined privacy spectrum requirements and aligning digital credentials with paper-based use cases. Security, threat, performance, and limitation analyses were conducted, with a proof-of-concept implementation showing practical efficiency through measured execution times. The experimental study presented in (Bhargav-Spantzel et al., 2010), tackles identity theft prevention by introducing a privacy-preserving multifactor verification method. The scheme leverages aggregate signatures on commitments and aggregate zero-knowledge proof of knowledge (ZKPK) protocols, resulting in short signatures and highly efficient proofs. Security is proven under the co-gap Diffie-Hellman assumption with bilinear maps. Compared to existing ZKPK techniques, the approach offers better performance, flexibility, and lower storage requirements for proving knowledge of multiple secrets. A smart manufacturing security framework that integrates anomaly detection with Zero-Knowledge Proofs (ZKPs) is presented (Salam et al., 2024).

Advanced preprocessing and deep learning methods achieved high anomaly detection rates (95% for temperature, 90% for pressure) with fewer false positives. Detected anomalies are verified using zk-SNARKs, ensuring confidentiality and robust validation with a 98% success rate. The approach outperforms traditional methods and sets a benchmark for secure, privacy-preserving industrial operations. Verifiable Anonymous Identity Management (VAIM) for the Human IoT, addressing privacy issues in blockchain-based IDM is presented in (Ra et al., 2021). The scheme enhances the claim identity model using ZKP for unlinkability, integrates a Blind Ordered Multi-Signature (BOMS) protocol for efficient anonymous verification, and applies practical ZKP algorithms like CL-Signature and zk-SNARKs. Performance evaluation and security analysis confirm efficient privacy protection and broader applicability compared to prior models. Study presented in (Huang and Lin, 2009) presents two unbounded non-malleable NIZK protocols to counter man-in-the-middle attacks in asynchronous networks. The first transforms a 5-round concurrent non-malleable ZK argument in the CRS model into a non-malleable NIZK argument with optimal round efficiency. The second simplifies an earlier CRYPTO’01 scheme using hidden unduplicatable set selection, resulting in a shorter CRS and more efficient proof statements.

Recent studies (Table 1) demonstrate that blockchain combined with Zero-Knowledge Proofs (ZKPs) enables secure, privacy-preserving, and decentralized frameworks for credential verification, identity management, and authentication. Decentralized storage systems like IPFS and selective disclosure mechanisms allow tamper-proof credentials while protecting sensitive user data. Self-Sovereign Identity (SSI), Privacy-Preserving Identity Management (PPIdM), and Blockchain Designated Verifier Proofs (BDVP) leverage ZKPs and cryptographic protocols to ensure anonymity, traceability, and regulatory compliance. In authentication systems, blockchain-integrated multi-factor authentication and distributed mechanisms reduce reliance on centralized servers while preserving privacy and proof authenticity. Applications in the Internet of Vehicles (IoV), truck platooning, and anonymous payment systems use ZKPs and consensus improvements to achieve secure, efficient, and private operations. Integration with machine learning in biometric authentication and smart manufacturing ensures accurate verification and anomaly detection with minimal data exposure. Despite these advances, challenges remain in computational overhead, privacy risks, scalability, regulatory uncertainties, and institutional adoption, highlighting the need for optimized, practical implementations across domains.

Table 1

Table 1. Summary of recent research in blockchain and zero-knowledge proof applications for identity verification.

3 Proposed methods

The presented study proposes a secure and privacy-preserving system for verifying candidate credentials in the corporate hiring process by integrating blockchain, zero-knowledge proofs (ZKPs), and decentralized file storage. The implementation technique comprised three primary components: credential preparation and signature, storage and anchoring, and zero-knowledge proof generation and verification. In the initial phase, prospective academic documents were submitted to a secure portal, wherein the granting university served as the certifying authority and digitally endorsed each credential utilising the Elliptic Curve Digital Signature Algorithm (ECDSA). This digital signature guaranteed validity and thwarted forgery. The security of the proposed system relies on the credibility of universities and academic institutions acting as certifying authority.

To enhance this sense of trust, the presented study employs a complete key management strategy that encompasses the signing, rotation, and revocation of cryptographic keys. Credentials from each institution are digitally signed via the Elliptic Curve Digital Signature Algorithm (ECDSA), which provides robust yet concise cryptographic assertions. To mitigate risks associated with prolonged key exposure, signature keys are issued with restricted validity and are frequently changed.

Revised public keys are disseminated using an on-chain decentralised registry, guaranteeing transparent accessibility for verifiers while maintaining backward compatibility with previously issued credentials. In events of compromise, institutions can promptly revoke keys by marking them in the register, with revocation events permanently documented on-chain for verification by recruiters and HR workers.

The registry establishes a governance framework that permits only recognised universities and authorised institutions to register keys via a consortium-led approval procedure. This inhibits unauthorised individuals from inserting harmful keys and guarantees transparency in credential validation. In the future, this system may develop into a DAO-based governance model, wherein academic consortia and regulators collaboratively manage onboarding, key rotation, and revocation regulations. The presented decentralised strategy enhances resilience to institutional compromise and fortifies the trust model within the recruiting ecosystem.

The signed credentials were subsequently stored in the InterPlanetary File System (IPFS) via the Pinata gateway, with each document producing a distinct content-addressed hash, facilitating tamper-proof and verifiable storage. To enhance confidence, the document hash was anchored on-chain by storing it in a smart contract deployed on the Ethereum Sepolia and Polygon Amoy testnets. To guarantee the enduring accessibility of credential data on IPFS, we employed pinning services like Pinata gateway in conjunction with redundant replication across several nodes. This method protects against data loss and guarantees uninterrupted access. Data integrity is preserved by anchoring both the document hash and Merkle root on-chain, enabling verifiers to identify any alterations. Collectively, these methods ensure continuous access while assuring verifiable validity. While ZKPs protect the confidentiality of credential data, information like transaction timestamps or IPFS access patterns may potentially reveal potential side-channel vulnerabilities. To resolve this, our architecture integrates methods like as batching and randomising proof submissions, utilising gateway proxies for IPFS queries, and implementing encrypted off-chain channels for the sharing of sensitive metadata. Looking ahead, the system can be extended with privacy-focused Layer-2 solutions such as zk-rollups, which would help obscure transactional patterns while preserving efficiency. Ultimately, during the verification step, ZoKrates, a zk-SNARK toolkit, was utilised to produce zero-knowledge proofs of credential authenticity while safeguarding the underlying data. Candidates produced lightweight proofs from their credentials saved on IPFS and presented them to HR officials, who verified the proofs on-chain by assessing their validity and the associated stored document hash. In this study, Merkle Trees are essential for the efficient organisation and verification of candidate materials. Universities digitally sign and upload academic records to IPFS, separately hashing each document and combining these hashes through Merkle Tree logic to generate a singular Merkle Root. This root signifies the integrity of the complete dataset and is recorded on the blockchain as a secure, tamper-proof reference. Should a candidate endeavour to submit modified or counterfeit credentials, the Merkle Root will fail to align, promptly indicating the discrepancy.

In this research, Merkle Trees play a crucial role in organizing and verifying candidate documents efficiently. When universities digitally sign and upload academic records to IPFS, each document is hashed individually, and these hashes are combined using Merkle Tree logic to produce a single Merkle Root. This root represents the integrity of the entire dataset and is stored on the blockchain as a secure, tamper-evident reference. If a candidate attempts to submit altered or fraudulent credentials, the Merkle Root will not match, immediately flagging the inconsistency. The use of Merkle Trees ensures that even with large sets of documents, only a minimal amount of data is needed for verification, keeping ZKPs lightweight and fast to process. Moreover, HR personnel do not need access to the complete dataset; instead, they can validate a specific credential using only a proof path. This approach enhances both privacy and performance while strengthening the system’s trust model.

The overall system architecture, illustrated in Figure 1, ensures end-to-end security and privacy. The process begins when a candidate requests document verification from their university administration. Once validated and digitally signed, the verified document is uploaded to IPFS, and its hash (along with the Merkle Root) is recorded on the blockchain as an immutable reference point. Parallelly, candidates access the Verify Xpert Portal, a recruitment interface where job listings are available. When applying for a job, the system checks whether a valid ZKP already exists; if not, a new proof is generated from the IPFS hash and Merkle Root of the digitally signed credentials and stored securely for future use. During verification, the system compares the candidate’s ZKP with the corresponding blockchain hash and Merkle Root. A successful match confirms the authenticity and integrity of the credentials, after which the hiring workflow automatically proceeds. This approach ensures candidate privacy, eliminates manual verification overhead, and removes dependency on intermediaries, enabling scalable, global, and secure hiring practices.

Figure 1

Figure 1. Proposed method architecture.

Algorithm 1

Algorithm 1. Credential Verification.

3.1 Merkle Tree Generation

3.2 Smart contract gas saving techniques

Concerns about gas-saving strategies in smart contract design are well-founded, as blockchain systems must strike a careful balance between security, efficiency, and cost-effectiveness. In our implementation, we adopted several gas-optimization techniques to minimize operational costs while ensuring system reliability. First Keccak256 hashing was employed for storage optimization, where only the document hash and Merkle root were stored on-chain instead of the complete credential databases. This approach significantly reduced costs, since storing fixed-length hashes is far less expensive than persisting raw data. In addition, auxiliary verification details were recorded through blockchain events rather than permanent storage, leveraging Ethereum and Polygon’s transaction logs to preserve verifiability while avoiding costly state changes. Additional optimizations were implemented via structure packing and data type improvement, with identifiers stored in compact formats like unit 32 or bytes 32. This allowed several variables to utilize a single storage slot, hence reducing gas consumption each transaction. The Merkle tree root anchoring is incorporated to supplant entire dataset storage or redundant verification, guaranteeing that simply limited on-chain computing was used for proof validation. The method was ultimately built to facilitate proof reusability, enabling certified zk-SNARKs to be stored and referenced in future transactions. This removed unnecessary verifications and significantly reduced petrol expenses in larger volume of recruitment situations. These solutions collectively enhanced the system’s cost effectiveness and scalability, while ensuring robust, privacy-preserving verification with zk-SNARKs.

4 Results and discussion

4.1 Experimental setup

The experimental evaluation used a Dell Inspiron 15 laptop equipped with an Intel Core i5 (10th Gen) processor and 8 GB RAM, running on Windows 11, reflecting the typical computing environment available to job seekers. The software stack comprised ZoKrates v0.8.1 for zk-SNARK proof generation and verification, Solidity v0.8.20 for smart contract creation, and the Truffle framework for contract compilation, migration, and testing. Ganache and MetaMask were utilised for local blockchain testing before deployment, while the Pinata API enabled safe uploads and retrievals of files on IPFS. Two platforms were chosen for blockchain deployment and validation: the Ethereum Sepolia Testnet, which simulates the Ethereum mainnet environment, and the Polygon Amoy Testnet, which simulates the Polygon mainnet environment. This dual-chain configuration facilitated a comparative assessment of performance, cost, and scalability within both ecosystems. The viability and efficacy of the proposed privacy-preserving recruiting verification system by implementing and testing essential components, including smart contract deployment, Zero-Knowledge Proof (ZKP) generation, and on-chain verification on Polygon and Ethereum. The study we conducted concentrated on essential operational measures, encompassing deployment cost, verification duration, system latency, transaction costs, proof size, scalability, and privacy preservation. These benchmarks are crucial for evaluating the practical feasibility of incorporating blockchain and ZKP technologies into corporate hiring processes.

4.1.1 Verification time (T_verify)

The time taken by the verifier to confirm the authenticity of the proof (Equation 1).

${\mathrm{T}}_{\text{verify}}={\mathrm{T}}_{\text{ZKP}}+{\mathrm{T}}_{\text{hash}}(1)$

Where T_ZKP is time to verify the zero-knowledge proof and T_hash is the time to validate the document hash on-chain.

4.1.2 Proof size (Sproof)

The size of the ZKP generated from the candidate’s document. The tools like ZoKrates, the proof size remains lightweight, it is just a few kilobytes, making it easy to transmit and store (Equation 2).

${\mathrm{S}}_{\text{proof}}\approx \mathrm{k}\text{\hspace{0.17em}KB\hspace{0.17em}where\hspace{0.17em}}\mathrm{k}\in \left[2,20\right](2)$

4.1.3 Scalability (S)

Assesses how well the system performs as the number of users grows. The below equation S is the scalability, N_users are the number of simultaneous users and T_system is the total processing time under load (Equation 3).

$\mathrm{S}={\mathrm{N}}_{\text{users}/}{\mathrm{T}}_{\text{system}}(3)$

The Table 2 presents a comparative insight into contract deployment costs and ZKP verification performance across both blockchain networks Polygon and Ethereum for deploying smart contracts. While both networks consumed identical gas for deployment (2,203,993 gas units), Polygon proved significantly more cost-effective due to its lower gas price and faster block time. Specifically, the deployment cost on Polygon was only ₹2, matching Ethereum’s cost in rupees, but offering superior transaction speed with a 2-s block time compared to 12 s on Ethereum. Furthermore, Polygon’s high TPS capability (7,000–65,000) and lower network congestion enable better scalability and throughput, especially during peak hiring seasons or bulk verifications. Ethereum, although equally reliable in terms of gas usage, suffers from slower processing and higher congestion, limiting its suitability for real-time verification systems in corporate environments. Our results demonstrate that Polygon significantly outperforms Ethereum in terms of cost, speed, and scalability, making it a more suitable choice for implementing high-throughput, low-latency verification systems.

Table 2

Table 2. ZKP Verification Performance across the platforms.

Blockchain’s transactional characteristics such as immutability, transparency, and decentralization are central to building trust in digital systems. Every transaction recorded on the blockchain is time-stamped, tamper-proof, and verifiable by anyone, which eliminates the risk of falsifying records. In this research these behaviours ensures that once a candidate’s academic document hash is stored on-chain, it cannot be altered or deleted, providing a reliable audit trail for recruiters. Figure 2 shows the various network metrics comparison of Ethereum and Polygon. Polygon exhibits remarkably greater efficiency with less block time and high transaction throughput. While Ethereum indicates a lesser gas cost per unit, both networks demonstrate a similar overall deployment cost approximately (₹2). The results comparison illustrates Polygon’s lesser congestion and faster transaction confirmation resulting in superior scalability for high-volume applications.

Figure 2

Figure 2. Comparison of various network metrics.

Table 3 compares the transactional characteristics of ZKP-based job applications and verification on Polygon (Amoy Testnet) and Ethereum (Sepolia Testnet). The transaction fee on Polygon was minimal (∼₹1) compared to a significantly higher cost on Ethereum (∼₹2,500), even though gas usage was relatively similar. The burnt fees were also negligible on Polygon, demonstrating its cost-efficiency. With a consistent block time of around 2 s, Polygon enables near-instant verification, which is critical for streamlining recruitment pipelines. Ethereum, on the other hand, not only incurs higher transaction fees but also experiences delays due to a 12-s block time and greater gas price volatility. These results reinforce the advantage of using Polygon for real-time, scalable, and low-cost Zero-Knowledge Proof verification in decentralized hiring systems. The transactional assures facilitate a scalable, efficient and trustless verification process, enabling the proof of authenticity without compromising private data precisely what contemporary recruitment systems demand.

Table 3

Table 3. ZKP Verification Performance on transactional (Operational) characteristics.

An overall evaluation of the proposed method is presented in Table 4, alongside various contemporary literature discussed. The analysis indicates that the proposed solution for secure candidate verification in corporate recruitment offers a highly appropriate and domain-specific method for implementing privacy-preserving technologies. The reviewed papers examine essential elements such as Zero-Knowledge Proofs (ZKP) and blockchain integration, with many differing in their scope and practical emphasis. References (Rahman et al., 2023) and (Zhang et al., 2017) illustrate robust identity frameworks make use of blockchain and zero-knowledge proofs; however, they fail to consider recruitment workflows or the integration of decentralized storage solutions such as IPFS. Introduces authenticated ZKPs, yet it fails to address document verification and does not prioritize cost optimization. Among them (Chi et al., 2023), demonstrates a degree of alignment with recruitment and provides scalable, cost-effective ZKP identity management; however, it still fails to address decentralized document storage. In contrast, our proposed method unifies all essential features such as candidate identity verification, credential authenticity, scalability, privacy, and cost-efficiency. Crucially, it includes IPFS for tamper-proof document access and leverages low-fee networks like Polygon Amoy, ensuring high performance with minimal overhead. This combination of completeness and practicality positions our solution as a robust advancement personalized to secure, real-world recruitment systems.

Table 4

Table 4. Comparative analysis and thematic evaluation of the proposed method.

In order to test the practicality of the experiment in a real-world setting, proofs were made for 20 candidate credential files of different sizes, ranging from 100 kilobytes to 2 megabytes, and the average creation times were recorded. In order to confirm the on-chain performance, smart contracts were installed on both the Ethereum Sepolia and Polygon Amoy testnets. On each network, verification delay and gas usage were recorded across 50 verification runs. Following Table 5 that, an analysis of expenses was carried out by monitoring the current market prices for gas Sepolia 20 to 25 gwei, Amoy 2 to 5 gwei. The associated costs of verification were determined in terms of USD equivalents. Additionally, the size of the proofs was measured. Zk-SNARK proofs regularly cut down within the range of 6–8 KB, which further supports the fact that they are lightweight and therefore ideal for transmission and storage. Lastly, in order to assess scalability, a stress test was conducted. This involved the simulation of a mass recruiting scenario with 100 candidates at the same time. The findings revealed that Polygon performed better than Ethereum, providing substantially reduced latency and greater throughput. This demonstrates that Polygon is a feasible option for large-scale implementation in recruitment operations that are used around the world.

Table 5

Table 5. Experimental results with different file size.

The experimental valuation exposed several significant findings regarding the effectiveness of zk-SNARK-based candidate credential verification. The time required for proof generation exhibited a marginal increase as credential file sizes escalated from 100 KB to 2 MB, although consistently remained under 3.5 s on common consumer-grade PCs. This illustrates the practicality of the method for real-world candidates lacking access to high-performance hardware. Verification performance varied amongst platforms, with Polygon recognizing a significant 25%–30% improvement in verification delay relative to Ethereum, mostly with decreased block time and increased throughput. Gas consumption during proof verification was marginally elevated on Ethereum, resulting in increased operational costs relative to Polygon. Notably, the proof size consistently ranged from 6 to 8 kB, irrespective of the credential file size, underscoring the scalability benefit of zk-SNARKs, as verification efficiency is preserved regardless of input size. Together, these findings indicate that although Ethereum provides strong decentralization and guarantees trustable system. Polygon is p a more cost-effective and practical choice, transformation is particularly gainful for wide corporate recruitment contexts for rapid and high verification of economical processing.

4.2 Statistical analysis with mythril

A thorough security assessment of the implemented smart contracts was conducted using Mythril, an open-source security analysis framework for Ethereum based contracts, to guarantee the system’s robustness and reliability. Mythril utilizes symbolic execution, taint analysis and control flow inspection to identify prevalent vulnerabilities that may jeopardise contract integrity. The investigation results shown in Table 6, it clearly shows the absence of reentrancy vulnerabilities, integer overflow, integer underflow, timestamp dependency, transaction ordering dependency and Denial of Service (DoS). Moreover, unregulated external calls a prevalent source of vulnerabilities in smart contracts were not identified, as our system architecture intentionally circumvents Ether forwarding and reduces dependence on answers from untrusted contracts. Mythril has certified that access control methods were effectively implemented, guaranteeing that credential anchoring and verification functions could solely be executed by authorized entities, including universities, candidates, and HR verifiers. Collectively, these findings indicate that the proposed framework is robust against many high-impact attack routes, establishing a solid security basis for candidate credential verification in sensitive recruitment contexts.

Table 6

Table 6. Mythril results.

5 Conclusion

The proposed method provides a practical solution to a real and growing problem in the global hiring process verifying academic credentials in a way that is secure, fast, and respectful of privacy. By bringing together Zero-Knowledge Proofs, blockchain, and decentralized file storage, the system ensures that candidate data remains tamper-proof, verifiable, and private. Candidates no longer need to share full documents to prove their qualifications, and recruiters can trust what they see without dealing with manual checks. Our evaluation shows that while Ethereum offers strong security, Polygon is better suited for large-scale use due to its speed and lower cost. This makes the framework highly relevant for companies conducting cross-border hiring and handling high application volumes. By reducing verification time and improving trust on both sides, this approach supports the future of digital recruitment one that values transparency, privacy, and efficiency in equal measure.

6 Future work

The proposed framework demonstrates the feasibility and effectiveness of ZKP-based candidate credential verification, several limitations remain. The current prototype was evaluated on testnet environments (Ethereum Sepolia and Polygon Amoy), which may not fully reflect mainnet network congestion or transaction volatility under real-world loads. Although zk-SNARKs offer efficient and privacy-preserving verification, their trusted setup requirement introduces a potential dependency that could be eliminated by future integration of zk-STARKs for quantum resistance. Additionally, the evaluation was conducted on limited hardware and does not yet account for large-scale institutional adoption scenarios involving thousands of verifications simultaneously.

Integrating Artificial Intelligence (AI) approaches to enhance the blockchain–ZKP ecosystem is an innovative expansion of the suggested architecture. AI-driven anomaly detection might be used to find strange patterns of credential use, such repeated verification attempts from suspect sources (Ressi et al., 2024). This would make fraud protection systems stronger. In order to maximize efficiency during execution on Ethereum and Polygon, machine learning models can enhance techniques for deploying smart contracts by dynamically forecasting and minimising gas costs. The credential validation process might also be customised according to organizational needs, candidate profiles, and regional compliance standards through the use of AI-powered adaptive verification workflows. Further, work will include large-scale stress testing of the system under mass recruitment scenarios to evaluate throughput, latency, and reliability. In future work, the presented experiment would integrate W3C Verifiable Credentials (VCs) and Decentralized Identifiers (DIDs) to strengthen interoperability with global identity systems. We also aim to improve credential revocation and re-anchoring mechanisms to support privacy-preserving updates. To enhance accessibility, the system will incorporate a user-friendly interface and candidate support modules that abstract the underlying cryptographic complexity of ZKPs. Together, these enhancements will make the framework more scalable, secure, and practical for real-world, cross-border recruitment workflows.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

AR: Conceptualization, Investigation, Methodology, Writing – original draft. CV: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review and editing. RM: Conceptualization, Formal Analysis, Investigation, Resources, Software, Validation, Writing – original draft, Writing – review and editing.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that Generative AI was used in the creation of this manuscript. Used for doing grammar corrections and to enhance the language quality.

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