Enhancing Age Verification with Mobile Driver’s Licenses (mDLs)

The advent of Mobile Driver’s Licenses (mDLs) has revolutionized identity verification, making it easier, more secure, and privacy-preserving to validate age and other credentials. This post focuses specifically on the age verification process using mDLs while excluding additional transaction-specific details.

Age Verification Options with mDLs

With mDLs, you can request one of several data elements to verify an individual’s age. Each method offers varying levels of privacy, ensuring flexibility to match the specific needs of a transaction.

1. Requesting Date of Birth

• Attribute: birth_date

• Example: 15-08-1990

• Process: The mDL provides the date of birth. You calculate the age based on the current date.

• Privacy Note: Least privacy-preserving as it reveals the exact date of birth.

2. Requesting Age in Years

• Attribute: age_in_years

• Example: 33 years old

• Process: The mDL directly provides the age in years, eliminating the need for calculations.

• Privacy Note: Better privacy-preserving as it provides only the age without disclosing the full birth date.

3. Requesting Age Over NN

• Attribute: age_over_NN

• Example: age_over_18, age_over_21, age_over_65

• Process: The mDL confirms whether the individual’s age exceeds a specific threshold (e.g., 18 or 21). The result is a simple true or false.

• Privacy Note: Most privacy-preserving as it avoids sharing exact age or birth date.

4. Requesting Birth Year (Least Accurate)

• Attribute: age_birth_year

• Example: 1990

• Process: The mDL provides the birth year, and you calculate the age using the current year. This method lacks precision as it excludes the birth month and day.

• Privacy Note: Moderate privacy-preserving but less accurate.

Privacy Considerations

When selecting an age verification method, prioritize privacy preservation. Below is a summary of the privacy levels associated with each data element:

Data ElementPrivacy Levelbirth_dateLeast privacy-preservingage_in_yearsBetter privacy-preservingage_over_NNMost privacy-preserving

Recommendations

• Most Privacy-Preserving Method: Use age_over_NN whenever possible. This method reveals only whether the individual meets the required age threshold.

• Better Privacy-Preserving Method: Use age_in_years if the exact age is necessary, avoiding the full date of birth.

• Least Privacy-Preserving Method: Use birth_date only when the exact date is mandatory for compliance or the transaction.

Identity Verification with mDLs

To ensure the individual using the mDL is the person associated with the credentials, you may need to request a portrait along with age-related attributes. Example combinations include:

• age_in_years + portrait

• age_over_NN + portrait

• birth_date + portrait

State-Specific Requirements

Keep in mind that some states may have additional requirements for age and identity verification. While this guide provides general recommendations, always adhere to state-specific guidance to ensure compliance.

Conclusion

By leveraging mDLs for age verification, organizations can strike a balance between security and privacy. Whenever possible, choose the most privacy-preserving method suitable for the transaction’s requirements. mDLs not only simplify the process but also empower individuals by limiting the disclosure of unnecessary personal information.