HKD_FACTOR_50: Secure Data Valuation and Sharing via Homomorphic Encryption | IJCT Volume 13 – Issue 5 | IJCT-V13I5P42
International Journal of Computer Techniques
ISSN 2394-2231
Author
Michael S. Yang^
Abstract
Data marketplaces and collaborative analytics require a way to estimate the value of contributed records without exposing the records themselves. This paper presents HKD_FACTOR_50, a protocol architecture that com-bines task-specific marginal-contribution valuation, addi-tively homomorphic aggregation, policy-bound release, and an auditable payment ledger. The construction does not claim that encryption makes arbitrary machine-learning training private. Instead, it protects a deliber-ately restricted interface: contributors encrypt quantized valuation statistics, an evaluator computes approved linear aggregates on ciphertexts, and a threshold or designated decryptor releases only authorized totals. We define cor-rectness, confidentiality, integrity, and audit requirements; describe leakage and collusion boundaries; and provide a complete executable Python reference demonstration based on the Paillier cryptosystem. The program verifies encrypted weighted aggregation against plaintext compu-tation and rejects tampered ledger entries. The reference code is pedagogical rather than production-hardened, but it makes the protocol’s data flow and invariants repro-ducible. HKD_FACTOR_50 therefore supplies a concrete baseline for privacy-preserving data valuation while iden-tifying the additional controls required for deployment: authenticated transport, robust key custody, range proofs, differential-privacy release, and independently reviewed cryptographic libraries.
Keywords
Data valuation; homomorphic encryption; authenticate participants, and control what is released. privacy-preserving computation; Paillier cryptosystem; secure data sharing; auditability; data marketplace; Shapley value.
Conclusion
HKD_FACTOR_50 defines a reproducible architecture for valuing and sharing approved data statistics without revealing each submitted value to the evaluator. Its cen-tral design choice is restraint: use additive homomorphic encryption for a bounded linear interface, make valuation and release policies explicit, and treat audit, authentica-tion, output privacy, and governance as separate require-ments. The included program demonstrates the algebraic core and tamper-evident transcript. A production imple-mentation should replace the demonstration primitives with reviewed libraries and independently validate both cryptographic parameters and marketplace policy.
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How to Cite This Paper
Michael S. Yang (2026). HKD_FACTOR_50: Secure Data Valuation and Sharing via Homomorphic Encryption. International Journal of Computer Techniques, 13(5). ISSN: 2394-2231.
