Data de-identification is essential in an information-intensive world. It removes or masks anonymously personally identifiable information (PII) from data sets. In addition to PII, this method also removes protected health information (PHI) to protect an individual’s privacy.

With data de-identified, it’s nearly impossible to classify information and identify an individual. Data de-identification is generally required as per HIPAA compliance. Moreover, the de-identified data is usually more useful than data with all the personal information. Let’s walk through more such facts about data de-identification and the methods used for the same.

5 Aspects You Everyone Should Know About Data De-Identification

Given the importance and spread of data, it’s essential for everyone to understand data de-identification and its dynamics.

Whether approached from a security perspective, sharing, ethics, or governance, these five facts about data de-identification are important.

1.    HIPAA Necessitates Data De-identification

The Health Insurance Portability and Accountability Act or HIPAA (1996) necessitates de-identifying data before it becomes public information. HIPAA also prescribes two methods that ensure effective data filtration;

  • Expert Determination
  • Safe Harbour

Expert determination uses statistical and scientific methodologies to minimize identification risks. The Safe Harbor method has a checklist of 18 parameters and every box should be ticked to say compliant.

Mostly, healthcare organizations have to comply with the HIPAA rules, hence, they employ these methods to de-identify healthcare datasets.

2.    Healthcare Data is Intricately Intertwined

All healthcare data is so profusely connected that separating every identifiable element from the general data can impact research, diagnosis, and treatment.

Some conditions are age-specific and removing the age from this data won’t be effective as the same information can be reidentified from the condition. Similarly, masking a patient’s gender from the data can also be redundant in conditions that are gender-specific.

3.    Data De-Identification Protects More Than Human Privacy

Data de-identification masks personal information, true. But it is also used for other purposes like business research and statistical analysis, mining companies de-identify data to protect the location of their mining sites.

Moreover, environmental protection agencies need data masking to protect endangered species from exploitation, poaching, etc. Data has immense value and every type of data can be exploited.

Hence, data de-identification can vary according to the purpose and industry.

4.    Data Masking is Different from Data De-Identification

Data masking may sound similar to data de-identification. The former involves replacing personal information with random information, but the latter involves removing the data altogether.

In masking, the information is replaced with random but identifiable values. Only the individuals who have set the values are able to decrypt the information. This is why data masking has limited utility because even with unauthorized access, identifying the information behind the mask is possible.

With data de-identification, the data in question is deleted or altered to the extent that it cannot be linked to any individual or entity. Data de-identification is done through different statistical techniques and methods.

5.    Process to De-Identify Data

To de-identify data from a large healthcare dataset, professionals employ technical solutions and software to remove identifies like;

  • Name
  • Address
  • Gender
  • DOB
  • Location

These solutions remove, encrypt, and code data for de-identification. While these methods encrypt data, in the future if the same data is required, it won’t be easy to decipher the same.

Data de-identification is a legislatively complex and intertwined process. Once de-identified, it’s essential to ensure zero risk of reversibility. If it’s reidentified, there is no point in altering and deleting the information.

Best Methods for Data De-Identification

To ensure that a healthcare organization minimizes identification risk and extracts the best value from data, it must employ several techniques and methods to ensure consistent results. Here are a few techniques to follow;

  • Removing Personal Identifiers: Personal information elements like name, address, social security number, gender, and DOB must be removed completely. Within these, advanced-level algorithms are used to ensure the elimination of data thus minimizing the risk of reversibility and re-identification.
  • Generalization: Some specific sets of information in healthcare datasets can be generalized. For example, we can replace specific addresses with an area range. However, this is done while ensuring data and statistical integrity and following a seamless distribution of data.
  • Masking: In masking, personal information is replaced with symbols and characters. Masking utilized cryptographic principles to preserve the data sensitivity while hiding the underlying values. Cryptographic hashing algorithms are used here to create irreversible and unique representations of personal identifiers.
  • Perturbation: Perturbation is the controlled introduction of random noises in the dataset. This is done to balance the dataset’s utility and privacy. Data science experts use differential privacy to introduce noise strategically integrated into data to prevent its reconstruction. However, this is also done while ensuring that the existing data insights are not disturbed.
  • Synthetic Data Generation: Another method for healthcare dataset de-identification involves creating new datasets mirroring the original ones. The synthetic datasets have the same statistical properties to ensure accurate results and analysis. For this, data scientists use Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). They learn the existing patterns and correlations to create synthetic datasets without any personal information identifiers.


Data de-identification is a critical procedure for ensuring the protection of unauthorized access, and unlawful use of personal data. Specifically important for healthcare data, this process ensures no personally identifiable information lands in the hands of individuals other than those closely related to the data.

With modern technology, de-identifying data has become easier as we are using healthcare AI tools and technologies for this purpose at Shaip. Let us protect the personal information you generate in your organization helping you stay compliant with the laws and regulations and build trust in the community.

About Author: Hardik Parikh


With more than 15 years of experience creating and selling innovative tech products, Hardik is an accomplished expert in the field. His current focus is building and scaling Shaip’s AI data platform, which leverages human-in-the-loop solutions to provide top-quality training datasets for AI models.

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