Derived Identity and System Personalization: A Vision Toward the Physical-Digital Link Through Robust Cryptography

Abstract

The concept of a derived identity represents both a conceptual and practical model in which an individual’s physical and personal attributes—including biometrics, documentary data, and personalization signals obtained from devices such as printers—are transferred into the digital realm through robust authentication processes. This convergence allows the creation of a unique digital fingerprint, comparable to DNA in the physical world, ensuring traceability, fraud resistance, and end-to-end cryptographic control. This article proposes a reference architecture for building derived identities, grounded in recognized technical security guidelines and normative recommendations, and presents a practical validation example in Python code.

1. Introduction

The key challenge of identity in the digital world is: how to derive secure and trustworthy credentials from original physical attributes. The notion of a derived identity seeks to solve this dilemma through a cryptographic link between the initial enrollment data and a unique digital credential.

The process incorporates personalization elements—such as the use of printers and local devices—that act as additional sources of entropy and authenticity factors tied to the individual’s physical presence. A properly designed derived identity becomes a singular entity, resistant to cloning and fraudulent reuse.

1. Conceptual Foundations 2.1. Derived Identity

A derived identity is the digital credential resulting from a derivation process based on previously verified attributes. It does not replace the original identity but rather complements it as a representation in electronic environments.

2.2. Input Attributes

Documentary Data: name, date of birth, unique identifier.

Biometrics: templates normalized under international standards.

Personalization Data: information from physical devices involved in the process, such as a printer’s serial number or print profile.

2.3. Cryptography as the Basis

The union of attributes is performed through robust hash functions and secure key derivation, producing a digital fingerprint resistant to collision and replay attacks.

1. Reference Architecture

Local Capture: collection of attributes and personalization in a controlled environment.

Canonical Construction: deterministic ordering of data to avoid ambiguities.

Derived Digital Fingerprint: application of a hash algorithm (example: SHA3-256) to generate the unique identifier.

Cryptographic Binding: use of key derivation (HKDF) and digital signature with approved algorithms (example: ECDSA P-256).

Lifecycle Management: inclusion of revocation and re-enrollment mechanisms, considering device replacement or expiration.

1. Role of Printers and Local Devices

Printers constitute a differentiating element in this model. The serial number, print configuration, or even micro-patterns naturally generated during the personalization process act as an additional binding factor between the individual and their digital identity. In this way, the system leverages both biometric and device-related factors to strengthen the derived identity.

1. Risks and Mitigations

Privacy: biometric templates must be encrypted and controlled, avoiding unnecessary exposure.

Cloning: the use of nonces and digital signatures prevents fraudulent reuse of credentials.

Interoperability: biometric templates and cryptographic algorithms must comply with internationally recognized standards to ensure compatibility across systems.

1. Python Example

The following code demonstrates a simplified process for generating and validating a derived identity.

"""
Derived Identity - Validation Example
Author: Antonio José Socorro Marín
Copyright (c) 2025. All rights reserved.
License: The use of this code is authorized provided that its creator is acknowledged.

Description:
This script demonstrates the construction of a derived identity using SHA3-256,
HKDF, and ECDSA P-256 digital signature.
"""

import json
import hashlib
import os
from base64 import urlsafe_b64encode
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.kdf.hkdf import HKDF
from cryptography.hazmat.primitives.asymmetric import ec
from cryptography.exceptions import InvalidSignature

def canonicalize_attributes(attrs: dict) -> bytes:
return json.dumps(attrs, separators=(',', ':'), sort_keys=True, ensure_ascii=False).encode('utf-8')

def sha3_256(data: bytes) -> bytes:
h = hashlib.sha3_256()
h.update(data)
return h.digest()

def derive_key(fingerprint: bytes, info: bytes = b'identity-derived-key', length: int = 32) -> bytes:
hkdf = HKDF(
algorithm=hashes.SHA3_256(),
length=length,
salt=None,
info=info
)
return hkdf.derive(fingerprint)

def generate_derived_identity(attributes: dict, biometric_template: bytes, device_info: dict, signer_private_key=None):
nonce = os.urandom(16)
canon = canonicalize_attributes(attributes)
device_blob = canonicalize_attributes(device_info)
blob = b'||'.join([canon, device_blob, biometric_template, nonce])
fingerprint = sha3_256(blob)
sym_key = derive_key(fingerprint, info=b'ID-Derived-SymKey', length=32)

signature = None
if signer_private_key:
    signature = signer_private_key.sign(fingerprint, ec.ECDSA(hashes.SHA256()))

return {
    'fingerprint_b64': urlsafe_b64encode(fingerprint).decode('utf-8'),
    'nonce_b64': urlsafe_b64encode(nonce).decode('utf-8'),
    'sym_key_b64': urlsafe_b64encode(sym_key).decode('utf-8'),
    'signature_b64': urlsafe_b64encode(signature).decode('utf-8') if signature else None
}

def generate_ecdsa_keypair():
sk = ec.generate_private_key(ec.SECP256R1())
pk = sk.public_key()
return sk, pk

if name == 'main':
attributes = {
"givenName": "Maria",
"familyName": "Perez",
"document": "ID-12345678",
"dob": "1985-04-12"
}
biometric_template = b'biometric-template-example'
device_info = {
"printer_serial": "PRN-001-XYZ",
"printer_profile_hash": urlsafe_b64encode(sha3_256(b'profile-v1')).decode('utf-8')
}

sk, pk = generate_ecdsa_keypair()
derived = generate_derived_identity(attributes, biometric_template, device_info, signer_private_key=sk)
print("Derived Identity:", json.dumps(derived, indent=2, ensure_ascii=False))

1. Conclusions

The derived identity represents an essential step in building secure authentication systems in an increasingly digitalized world. By integrating physical, biometric, and device personalization data under a robust cryptographic framework, a unique and verifiable digital fingerprint is obtained. The proposal presented here establishes the foundations for interoperable, scalable systems aligned with recognized technical and regulatory best practices.