Data De-Identification Services

Data de-identification services are a critical process for organizations working with sensitive data sets. Whether you’re working with medical records, financial data, or personal information, it’s important to protect the privacy of individuals and comply with data privacy regulations. In machine learning, de-identifying data is essential for building accurate and ethical models. Come work with us to keep your organization in compliance with HIPAA guidelines.

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What is Data De-Identification Services?

Data de-identification services are removing or altering specific identifying information from a data set to ensure that the resulting data set cannot be linked back to a specific individual. This can include a person’s name, address, social security number, or biometric identifiers.

Why is Data De-Identification Important?

It is important for many reasons, especially in the context of training machine learning algorithms.

  • Privacy. In today’s digital age, personal information is incredibly valuable, and individuals must have control over how their information is used. Organizations can help protect sensitive information from misuse or abuse by de-identifying data.
  • Security. With so much personal information, de-identifying data will help your organization minimize the risks and consequences to the privacy of individuals from potential data breaches.
  • Legal and regulatory requirements. Data de-identification is a statutory requirement in several data privacy laws, including HIPAA and GDPR. Organizations must de-identify their data or otherwise face fines and legal action.
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What PII Data-elements should be De-identified?

De-Identified Data Elements
Data Element Explanation/Example
Names Full name, first name and last name, and any initials.
All telephone numbers Fax number
Geographical Subdivisions Street address, city, county, precinct, zip code, and geocodes, except for the first three digits of a zip code if it has a population of more than 20,000.
Dates Birth date, admission date, discharge date, date of death, and any ages over 89.
Telephone Numbers Includes all types of telephone numbers, such as home, cell, or work numbers.
Social Security Numbers Details such as individual's social security numbers, which is a unique identification number issued by the U.S. government.
Medical Record Numbers It includes numbers assigned to a patient's medical record by healthcare providers.
Health Plan Beneficiary Numbers These are numbers assigned to a patient's health insurance plan.
Account Numbers These are numbers assigned to financial accounts or credit cards.
Certificate/License Numbers They are numbers assigned to professional certificates or licenses.
Vehicle Identifiers It includes numbers or codes assigned to vehicles, such as license plate numbers or VIN numbers.
Device Identifiers Includes numbers or codes assigned to devices, such as smartphones or laptops.
URLs Website links or addresses.
IP Address Numbers Numbers assigned to internet-connected devices.
Biometric Identifiers This includes unique physical or behavioral characteristics, such as fingerprints or voiceprints.
Full-Face Photographic Images It includes pictures of individuals that show their entire faces.
Other Unique Identifying Number/Code Any other type of unique identifier not mentioned above, such as employee identification numbers or customer account numbers.
Any other unique identifying number, characteristic, or code

What PHI Data Elements Should Be De-identified?

PHI Data De-identification, also known as PHI Data Anonymization, is the process of removing or obscuring any information in a medical record that can be used to identify an individual. This includes any data created, used, or disclosed during the provision of medical services, such as diagnosis or treatment. Protected Health Information (PHI) refers to any data that can contact, locate, or identify an individual. 

Some examples of HIPAA identifiers or data elements that might be used to identify an individual and which must be de-identified include:

  • Medical images, records, health plan beneficiary, certificate, social security, and account numbers.
  • Any date directly linked to a person, such as date of birth, discharge date, date of death, and administration.
  • Any payment associated with providing healthcare services in the past, present, or future.
  • An individual’s past, current, or future health condition.

How is Data De-Identification Performed?

De-identification depends on the specific data elements that need to be removed or altered. The process often involves removing or obscuring any unique identifying number or characteristic that could link the data to a specific individual, including social security numbers, biometric identifiers, or street addresses. When conducting data de-identification for your machine learning project, we follow these steps:


Data Collection

Our data collection services start by collecting the data needed for the project. Depending on the project, the data collected may include various elements, such as personal information, medical records, or financial data.


Identify the Data Elements

Data annotation and data lebeling identify specific PII and PHI data elements that could be used to identify an individual, including names, social security numbers, dates of birth, or biometric identifiers.


Remove Identifying Information

The identified data elements are removed from the data set. Depending on the dataset’s needs, we approach the process using different techniques, including masking or encryption.

Add Noise

Add Noise

The step is not mandatory but can help improve the security of the data. We may add noise, including adding random values or non-individual specific data to the dataset making it more difficult to link PHI and PII data back to specific individuals.

Verify Data Quality

Verify Data Quality

The data de-identification process should not compromise the technical usability of the dataset. Our team of experts will run tests to verify the quality of your datasets to ensure they can still be used for training the ML models.

Re-Evaluate the Data Set

Re-Evaluate the Data Set

We will re-evaluate the datasets by looking at the data from various angles, including assessing their suitability based on the project’s requirements.

Types of data De-Identification

As a reputable data de-identification service providing removal or masking of all potential identifiers from your datasets, we will use one of the three main methods to identify your data.

    HIPAA safer harbor

    It involves removing specific identifying information from a data set, including names, addresses, social security numbers, and other unique identifying characteristics. This helps organizations comply with HIPAA regulations and protect sensitive patient data. The approach can be helpful to covered entities that need to de-identify protected health information, allowing the data to be used for research and other purposes.

    Elapsed day approach

    The method involves shifting or masking dates in a data set to reduce the risk of identifying a specific individual. For instance, the date of birth or admission date may be shifted by a certain number of days or months, helping to protect sensitive medical data while maintaining the overall structure of the data set. However, it is important to ensure that the shift does not affect the integrity or accuracy of the data.

    SANT method

    The Systematic Analytic Network Tool (SANT) method uses advanced statistical techniques to identify and remove any unique identifiers in a data set. SANT involves a rigorous analysis of the data set to identify potential unique identifying information and then applying statistical techniques to remove or modify the information as needed. This method requires significant expertise and resources but can provide a highly effective way to de-identify sensitive data sets.

    Why Choose Us?


    AnnotationBox is a leading data de-identification service provider with a reputation for being the best in the industry. If you are looking for someone to carry out your data de-identification project, here is why you should work with us:



    Our team of experienced data scientists and privacy experts are well-versed in the latest data de-identification techniques and regulations. They will help you choose the most appropriate method of de-identification specific to your dataset and ensure that the resulting data fully complies with relevant regulations and guidelines.



    We take a flexible and scalable approach to data de-identification. Whether clients need to de-identify a small data set for internal research purposes, or a large data set for publication or sharing, AnnotationBox will tailor the de-identification process to meet your specific needs and budgets.

    Data Security and Confidentiality

    Data Security and Confidentiality

    AnnotationBox strongly emphasizes data security and confidentiality. All data handling and processing is carried out in compliance with the highest data security and privacy standards, including HIPAA, GDPR, and other relevant regulations. Clients can be confident that their sensitive data is in safe hands.

    Full-house services

    Full-house services

    We offer a range of additional services that can help our clients get the most out of their de-identified data sets. These include data labeling, annotation, and analysis services, which you leverage to get valuable insights into the data and support further research and development.

    How It Works


    STEP : 1

    Defining The Data Requirements



    STEP : 2

    Determine The Data Collection Methods & Tools


    STEP : 3

    Staff Onboarding & Training


    STEP : 4

    Pilot Data Collection



    STEP : 5

    Cleaning & Quality Checks Of Pilot Data


    STEP : 6

    Client Feedback


    STEP : 7

    Main Project Data Collection



    STEP : 8

    Quality Checks


    STEP : 9

    Client Feedback

    Key Features of Our Data De-identification Services



    Our team of experts with vast knowledge provides multiple levels of quality control, ensuring the resulting data meets your needs.

    Proven Track record

    Proven Track record

    We have handled over 50+ million PHI and PII data elements de-identification, giving users a proven platform for effective HIPAA de-identification.

    Enhanced Data security

    Enhanced Data security

    The data’s security is enhanced, ensuring that data formats are preserved throughout the process.



    De-identify data of any size without worrying about the quality of the outcome.

    Single Optimized Platform for Data Integrity

    Single Optimized Platform for Data Integrity

    Achieve data integrity across various systems and geographies through a unified data anonymization process.

    Areas that benefit from Data De-Identification

    De-identified data can be used in various applications across various industries. Some of the most common areas where de-identified data is used include

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    Healthcare and medical research

    De-identified medical records and clinical trial data can be used to study disease prevalence and treatment outcomes, identify potential risk factors and develop new treatments and interventions.

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    Finance and banking

    In the financial sector, de-identified financial data can detect patterns and trends in consumer behavior, inform investment decisions, and detect fraudulent activity.

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    Marketing and advertising

    Consumer data can be de-identified and used to develop targeted advertising campaigns and identify market trends.

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    Government and public policy

    Policymakers and governments can use de-identified census data and other demographic information to inform policy decisions and allocate resources.

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    Academic research

    In academic research, data can be de-identified, shared, and analyzed across institutions and disciplines, enabling collaboration and innovation.

    Graphic icon representing machine learning and artificial intelligence with a circuit and a cogwheel brain.

    Machine learning and artificial intelligence

    Training algorithms and models with de-identified data can help protect sensitive information about specific individuals, improving the accuracy and effectiveness of these systems.

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