PRE-COMMITMENT PRIVACY RISK INTELLIGENCE: MACHINE-LEARNINGGUIDED ADAPTIVE PROTECTION FOR VERIFIABLE BLOCKCHAIN CREDENTIALS

Abstract

Blockchain-based credential verification provides durable integrity and decentralized validation, but the persistence of blockchain commitments can make premature disclosure of sensitive attributes difficult to reverse. This study developed a pre-commitment privacy risk intelligence framework that assessed potential information leakage before credential commitment and used the estimated risk to guide adaptive protection. Synthetic credential records and simulated attacker-side information were employed to characterize privacy exposure through uniqueness, re-identification susceptibility, cross-source linkage, attribute inference, and attributeinteraction effects. These indicators were transformed into predictive features and evaluated using machinelearning models for continuous privacy-risk estimation and categorical risk assessment, followed by an optimization stage that considered privacy reduction and retained utility. In the reported training experiment, the gradient-boosted regression model achieved a mean absolute error of 0.03290, root mean square error of 0.04645, and coefficient of determination of 0.84398. Logistic regression provided the strongest classification performance, attaining 85.63% accuracy, 81.33% recall, an F1-score of 82.44%, and a privacy false-negative rate of 18.67%. A subsequent evaluation of 1,100 records reduced the mean privacy-risk score from 0.81826 to 0.67337, corresponding to a 17.71% relative reduction, while maintaining a mean utility score of 0.84227. The findings indicated that pre-commitment privacy assessment could support risk-sensitive protection decisions while retaining substantial utility for verifiable credential processing.