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Biometric Authentication Has Become A Cornerstone Of Modern Security Systems Due To Its Uniqueness And Resistance To Forgery. However, Traditional Biometric Verification Methods Often Raise Concerns Regarding Privacy Leakage, Data Misuse, And Template Theft. This Work Proposes A Privacy-preserving Biometric Verification Framework Using Handwritten Random Digit Strings As An Authentication Factor. In The Proposed System, Users Are Prompted To Write A Randomly Generated Digit Sequence, Combining The Inherent Individuality Of Handwriting Dynamics With The Unpredictability Of Random Strings. This Approach Prevents Replay Attacks, Minimizes The Risk Of Stolen Static Templates, And Enhances Resilience Against Impersonation Attempts. The Verification Process Leverages Machine Learning-based Handwriting Recognition And Feature Extraction Techniques, While Privacy-preserving Transformations Ensure That The Raw Biometric Data Is Never Stored Or Transmitted In Its Original Form. Experimental Evaluation Demonstrates That The Method Achieves A Balance Between Robust Verification Accuracy, User Privacy, And System Security, Making It A Promising Solution For Next-generation Secure Authentication Systems.