BLOCKCHAIN + HOMOMORPHIC ENCRYPTION FOR PRIVACY-PRESERVING CREDIT SCORING MODELS

Volume-07 Issue 05, May-2023 ISSN: 2456-9348 Impact Factor: 6.736 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [276] BLOCKCHAIN + HOMOMORPHIC ENCRYPTION FOR PRIVACY-PRESERVING CREDIT SCORING MODELS Vikas Reddy Mandadhi Bellevue University, [email protected] ABSTRACT Credit scoring is a critical component of financial decision-making, yet traditional models often require the disclosure of sensitive personal and financial information to multiple institutions, raising privacy, compliance, and trust concerns. This study proposes a privacy-preserving credit scoring framework that leverages blockchain technology in conjunction with homomorphic encryption (HE) to enable secure, decentralized, and auditable computation of credit scores. In the proposed system, users’ financial data is encrypted using HE, allowing credit institutions and fintech providers to perform computations on encrypted data without revealing raw information. Blockchain ensures tamper-evident logging, transparency, and verification of score computation while supporting regulatory compliance and auditability. The framework integrates off-chain HE computation with on-chain verification via smart contracts, providing a hybrid approach that balances computational efficiency with strong privacy guarantees. Comparative analysis demonstrates that the proposed model preserves user confidentiality, maintains scoring accuracy, and improves trust between stakeholders while satisfying data protection regulations such as GDPR and CCPA. This approach offers a scalable, secure, and regulatory-compliant solution for modern credit evaluation in decentralized financial ecosystems. Keywords: Privacy-Preserving Credit Scoring, Blockchain, Homomorphic Encryption, Decentralized Finance, Secure Computation, Data Confidentiality, Smart Contracts. 1. INTRODUCTION 1.1 Background on Traditional Credit Scoring Models Credit scoring has long been a cornerstone of financial decision-making, providing lenders with a quantitative assessment of an individual’s creditworthiness. Traditional credit scoring models, such as FICO and VantageScore, rely on historical financial data including payment history, outstanding debt, credit utilization, and public records. In recent years, alternative data sources—such as utility payments, mobile phone usage, and ecommerce transactions—have been incorporated to expand coverage, particularly for underbanked populations. While these models provide valuable insights, they require the collection, processing, and sharing of sensitive personal and financial information across multiple institutions, which can introduce privacy risks and regulatory challenges. 1.2 Privacy and Regulatory Challenges The reliance on centralized repositories of sensitive data exposes individuals to potential data breaches, unauthorized access, and misuse of personal information. Financial institutions must also comply with stringent privacy and data protection regulations, including the General Data Protection Regulation (GDPR) in the Volume-07 Issue 05, May-2023 ISSN: 2456-9348 Impact Factor: 6.736 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [277] European Union and the California Consumer Privacy Act (CCPA) in the United States. These regulations impose strict requirements for data minimization, user consent, auditability, and secure storage, creating operational challenges for conventional credit scoring systems. The need to balance accurate credit evaluation with privacy preservation is a growing concern, particularly as data sharing between banks, fintechs, and credit bureaus increases. 1.3 Motivation for Decentralized, Secure, and Auditable Frameworks Given these privacy and regulatory challenges, there is a clear motivation to develop credit scoring frameworks that provide secure, decentralized, and auditable mechanisms for score computation. A decentralized approach can reduce reliance on a single authority, enhance transparency, and allow multiple stakeholders to collaboratively validate computations without exposing sensitive raw data. Such frameworks can mitigate the risk of data leaks, support regulatory compliance, and increase trust between users, financial institutions, and credit bureaus. 1.4 Emerging Technologies: Blockchain, Homomorphic Encryption, and Federated Learning Recent advances in cryptography and distributed ledger technologies provide viable solutions for privacypreserving credit scoring. Blockchain offers an immutable, tamper-evident, and auditable ledger that can record the results of credit computations while ensuring transparency for authorized stakeholders. Homomorphic encryption (HE) enables computation on encrypted data, allowing credit institutions to perform scoring calculations without accessing raw user information. Additionally, federated learning allows multiple entities to collaboratively train predictive models without sharing raw data, further enhancing privacy. By combining these technologies, it is possible to design a credit scoring system that simultaneously preserves confidentiality, ensures auditability, and supports secure, decentralized decision-making. 2. OBJECTIVES AND SYSTEM REQUIREMENTS 2.1 Core Objectives The primary objective of the proposed framework is to enable privacy-preserving credit scoring that ensures confidentiality, transparency, and auditability while maintaining accuracy and decentralization. Privacy is achieved by allowing computations on encrypted user data so that financial institutions can generate scores without accessing sensitive raw information. Transparency is provided by recording immutable logs of scoring events on a blockchain, enabling authorized parties to verify computations. Auditability ensures that all credit scoring actions can be independently validated for regulatory compliance. Decentralization reduces dependence on a single authority, fostering trust among multiple stakeholders, including banks, fintechs, and credit bureaus. Accuracy remains a critical objective, ensuring that encrypted computation techniques do not compromise the predictive quality of credit scores. 2.2 Functional Requirements The framework must satisfy functional requirements that allow secure and automated computation of credit scores. Key functional capabilities include encrypted data computation, where user financial and alternative data are processed in encrypted form using homomorphic encryption libraries. Credit score generation must occur deterministically within the encrypted domain, with smart contracts managing computation requests and results. Verification functionality ensures that other participating institutions can confirm the correctness of scores without accessing underlying raw data. Logging of each computation event on a blockchain provides a tamper-evident record for audit and regulatory purposes. 2.3 Non-Functional Requirements Volume-07 Issue 05, May-2023 ISSN: 2456-9348 Impact Factor: 6.736 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [278] To operate effectively at scale, the framework must meet several non-functional requirements. Scalability ensures that the system can handle high transaction volumes and multiple concurrent scoring requests without degradation in performance. Resiliency provides continued operation despite network delays, node failures, or partial outages. Throughput measures the speed at which credit scores can be computed and verified across multiple institutions. Compliance with data protection regulations such as GDPR and CCPA is critical, ensuring that user consent, data minimization, and auditability requirements are met. The architecture must also support secure storage, encrypted communication channels, and proper access control mechanisms. 2.4 Security and Trust Assumptions The system assumes that participating institutions, including banks, fintechs, and credit bureaus, are semi-trusted: they will follow protocol specifications but may attempt to infer information from encrypted computations. Homomorphic encryption ensures that raw user data remains confidential, even if a node attempts to access it. Blockchain provides an immutable and auditable ledger to detect tampering or protocol violations. Proper key management, identity verification, and role-based access control are assumed to be in place, ensuring that only authorized entities can participate in score computation, verification, and audit processes. Table 1: Summary of Functional and Non-Functional Requirements Requirement Type Description Example Components Functional Privacy-preserving score computation Homomorphic encryption (HE) libraries, smart contracts Non-functional Scalability and throughput Permissioned blockchain, distributed nodes Regulatory Compliance with GDPR / CCPA Audit logs, encrypted storage 3. BACKGROUND 3.1 Credit Scoring Fundamentals and Conventional Computation Credit scoring is a quantitative method used by financial institutions to assess the creditworthiness of individuals and organizations. Traditional models, such as FICO and VantageScore, compute a score based on a combination of payment history, outstanding debt, length of credit history, credit utilization, and public records. These models rely heavily on centralized repositories of sensitive financial data. Recent innovations in alternative data usage, including utility payments, mobile phone usage, and e-commerce transactions, have allowed institutions to broaden coverage, particularly for underbanked populations. However, conventional credit scoring computations require access to raw data, creating potential privacy risks and exposing personal financial information to multiple stakeholders. 3.2 Blockchain Technology for Decentralized Trust and Immutable Audit Trails Blockchain technology provides a distributed ledger where data entries are cryptographically linked, tamperevident, and shared among authorized participants. In the context of credit scoring, blockchain enables the creation of a transparent, auditable system where credit computations and score verification events can be recorded immutably. Smart contracts can automate the verification and logging of computations, ensuring that no single participant can alter records unilaterally. The decentralized nature of blockchain reduces reliance on a central authority and promotes trust among banks, fintechs, and credit bureaus. Furthermore, blockchain provides robust audit trails that satisfy regulatory requirements for transparency and accountability. 3.3 Homomorphic Encryption: Basics, Fully and Partially HE, Computation on Encrypted Data Volume-07 Issue 05, May-2023 ISSN: 2456-9348 Impact Factor: 6.736 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [279] Homomorphic encryption (HE) is a cryptographic technique that allows computation on encrypted data without requiring decryption. Fully homomorphic encryption (FHE) enables arbitrary computation on ciphertexts, while partially homomorphic encryption (PHE) supports specific operations such as addition or multiplication. In the context of privacy-preserving credit scoring, HE allows institutions to compute credit scores over encrypted financial and personal data, ensuring that raw information remains confidential. The encrypted computation produces a ciphertext representing the credit score, which can be verified by other stakeholders without exposing sensitive inputs. HE thus provides strong privacy guarantees while enabling collaborative scoring across multiple institutions. 3.4 Related Works on Privacy-Preserving Credit Scoring and Secure Multiparty Computation Recent research has explored the application of secure multiparty computation (MPC), federated learning, and HE in privacy-preserving financial analytics. Several studies demonstrate that credit scoring models can be trained collaboratively across banks and fintechs without sharing raw user data. MPC-based frameworks allow institutions to jointly compute aggregate statistics and credit risk metrics while keeping inputs private. Federated learning enables decentralized model training using local data on each institution’s server, combining gradients or model updates without transferring raw data. However, these methods often face challenges related to computational efficiency, trust assumptions, and verifiability. Integrating blockchain with HE provides a hybrid solution, where computations are performed securely off-chain, and the results are anchored and verified on-chain, combining privacy, auditability, and decentralized trust. 4. PROPOSED FRAMEWORK 4.1 High-Level Architecture The proposed privacy-preserving credit scoring framework is designed as a multi-layered architecture that integrates blockchain and homomorphic encryption (HE) to achieve secure, decentralized, and auditable credit computations. The architecture consists of four main components: the blockchain layer, the HE computation layer, data providers (e.g., banks, fintechs), and credit institutions or bureaus that consume the computed scores. The blockchain layer maintains an immutable ledger of computation events, providing transparency and verifiability, while the HE computation layer performs all scoring operations on encrypted data to ensure privacy. Data providers supply encrypted user financial and alternative data to the HE layer, and credit institutions rely on blockchain-anchored proofs to verify the integrity of computed scores without accessing raw inputs. 4.2 Data Flow and Interaction Between Users, Banks, and Credit Bureaus In this framework, user data is encrypted using homomorphic encryption before submission to the computation layer. Banks or fintech platforms act as intermediaries, forwarding encrypted datasets to the HE computation nodes. The computation layer performs secure, encrypted processing to generate credit scores, which are then hashed and recorded on the blockchain. Credit bureaus or other financial institutions can retrieve the hash proofs from the blockchain to verify score integrity without decrypting sensitive user data. This workflow enables secure multi-party collaboration while maintaining user confidentiality and regulatory compliance. 4.3 Blockchain Roles: Ledger Storage, Smart Contracts for Logging, and Score Verification Blockchain serves as the backbone for decentralized trust and auditability in the proposed framework. Its primary roles include ledger storage, where computation logs and score hashes are recorded immutably, and smart contract execution, which manages logging events, enforces computation rules, and provides verification mechanisms. Smart contracts ensure that every credit score computation is auditable and can be validated by authorized Volume-07 Issue 05, May-2023 ISSN: 2456-9348 Impact Factor: 6.736 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [280] participants, preventing unilateral alterations and promoting confidence among stakeholders. Blockchain also enforces access control policies and provides a tamper-evident trail for regulatory review. 4.4 Homomorphic Encryption Roles: Encrypted Feature Computation, Secure Aggregation, Encrypted Scoring Models Homomorphic encryption allows computations to be performed directly on encrypted user data without decryption, preserving privacy throughout the process. Encrypted feature computation enables each institution to transform raw financial data into scoring inputs securely. Secure aggregation combines inputs from multiple institutions to calculate composite scores or risk metrics while maintaining individual data confidentiality. Encrypted scoring models compute credit scores deterministically, producing ciphertext results that can be validated via blockchain proofs without exposing raw data. This ensures strong privacy guarantees while supporting accurate credit evaluation. 4.5 Hybrid Approach: On-Chain Verification + Off-Chain HE Computation To balance efficiency, privacy, and transparency, the framework adopts a hybrid approach where intensive HE computations are performed off-chain, while blockchain is used for verification and logging. This model allows secure computation to scale without overloading the blockchain network, while still providing an immutable, auditable record of all transactions and computation results. The hybrid approach ensures that privacy is preserved, trust is decentralized, and regulatory requirements for transparency and auditability are met. 4.6 Participant Nodes: Banks, Fintechs, Regulators, Auditors The network consists of multiple participant nodes, each serving a specific role. Banks and fintechs act as data providers, submitting encrypted datasets for computation. Credit bureaus and other financial institutions act as consumers, verifying scores using blockchain proofs. Regulators and auditors may participate as observer nodes with read-only access, enabling compliance verification and oversight. Governance policies define node permissions, access controls, and responsibilities, ensuring a secure and trustworthy ecosystem. Figure 1: High-Level Architecture of Privacy-Preserving Credit Scoring System Volume-07 Issue 05, May-2023 ISSN: 2456-9348 Impact Factor: 6.736 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [281] 5. CREDIT SCORE COMPUTATION WORKFLOW 5.1 User Data Submission with HE Encryption The workflow begins with the user submitting financial and alternative data through a secure interface. Before transmission, all user data is encrypted using homomorphic encryption (HE), ensuring that sensitive information remains confidential during the entire process. This encryption allows subsequent credit score computations to occur without exposing raw data to any participating institution, thereby protecting user privacy while maintaining data integrity. 5.2 Distributed Secure Computation Using HE Once encrypted data is submitted, it is processed in a distributed secure computation environment. The HE computation layer performs the necessary arithmetic and statistical operations on ciphertexts to calculate credit scores. This distributed architecture allows multiple nodes—representing banks, fintechs, or computation providers—to participate collaboratively, without any node ever having access to plaintext inputs. The result is an encrypted credit score that preserves privacy while enabling accurate evaluation. 5.3 Blockchain Anchoring of Score Hashes and Computation Logs After the encrypted credit score is generated, a cryptographic hash of the result, along with computation logs, is recorded on a permissioned blockchain. Smart contracts manage the anchoring process, verifying that the computation follows the defined protocols and rules. Blockchain anchoring provides a tamper-evident and auditable record of all computations, ensuring transparency and allowing authorized parties to confirm the correctness of scores without accessing raw user data. 5.4 Verification by Credit Bureaus and Institutions Without Decrypting Raw Data Credit bureaus and other financial institutions can verify the correctness of credit scores by comparing the blockchain-stored hash and logs with the encrypted computation output. This verification process ensures integrity and trust without requiring decryption of the underlying user data. As a result, stakeholders can confidently rely on the computed credit scores for lending decisions while maintaining compliance with privacy regulations such as GDPR and CCPA. 5.5 Exception Handling and Audit Trail Creation The framework incorporates automated exception handling to flag inconsistencies or computation errors. Any discrepancies are logged on the blockchain with immutable records for audit purposes. This ensures that errors can be traced, analyzed, and resolved efficiently. The combination of HE and blockchain creates a complete audit trail, providing regulators, auditors, and institutions with verifiable evidence of privacy-preserving computation and score validity. Figure 2: Workflow Diagram of Blockchain + HE Credit Scoring Volume-07 Issue 05, May-2023 ISSN: 2456-9348 Impact Factor: 6.736 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [282] Table 2: Comparison of Traditional vs Privacy-Preserving Credit Scoring Feature Traditional Credit Scoring Blockchain + HE Scoring Data Privacy Raw data shared across institutions Fully encrypted computation Transparency Limited, central authority dependent Auditable via blockchain Trust Requires centralized trust Decentralized, tamper-evident Regulatory Compliance Challenging for data sharing Enforced via HE + blockchain logs Computation Cleartext, fast Encrypted, slightly higher latency 6. CONCLUSION This study presents a novel framework for privacy-preserving credit scoring that leverages the complementary strengths of blockchain and homomorphic encryption (HE). Traditional credit scoring systems often require the sharing of sensitive financial and personal data across multiple institutions, creating privacy risks and complicating regulatory compliance. The proposed framework addresses these challenges by performing encrypted credit score computations using HE, while anchoring computation proofs and logs on a permissioned blockchain to provide transparency, auditability, and decentralized trust. By enabling computations on encrypted data, the framework ensures that sensitive user information remains confidential throughout the entire scoring process. Blockchain integration guarantees tamper-evident records, verifiable computation, and immutable audit trails, reducing reliance on centralized authorities and enhancing trust among banks, fintechs, credit bureaus, and regulators. 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