As data collection increases, securing the information has also become more important. De-identification can reduce the risks of misusing personal data. This practice enhances data security across different sectors. In this blog, we have mentioned the methods, challenges, and benefits of data de-identification. But first, let’s know what it is.
Data de-identification hides your personal information

Data de-identification is a method of data masking. However, it removes Personally Identifiable Information (PII) from a document or other data record. Experts consider this the fastest and safest way to protect sensitive information found in datasets.

Often, people associate it with healthcare regulations like HIPAA. However, its value also applies to regulatory and data management frameworks like GDPR and CCPA. As a result, organizations should consider implementing data de-identification to enhance privacy protection or data security. 

Furthermore, the process enables the use of information in a dataset for any authorized internal or external purpose without compromising an individual’s privacy. By applying these techniques, many organizations can safeguard privacy, build trust, and enhance their competitive edge.

Data De-identification In Different Domains

De-identification has a vital role in many domains, such as finance, research, health service, marketing, and government.

The image shows different industries where data de-identification is used.


Financial institutions use de-identification to protect an individual’s data while detecting fraud and analyzing risk assessments.


Scientists can share datasets for collaborative research while obeying guidelines and privacy regulations.

Health Service

In the healthcare industry, de-identifying data allows researchers to study medical records. In addition, this will enable them to learn disease patterns, treatment outcomes, and public health trends while protecting patient privacy.


De-identifying helps companies to analyze customer data without compromising their privacy. However, it helps to improve products and services.


It enables agencies to share sensitive data for policy analysis while complying data protection laws.

How De-Identify Process Works?

It is the process of removing identifiers from personal information. So it is not possible to identify the person. The process is important to protect privacy and prevent identity theft. 

Furthermore, when de-identifying data, consider the type of data. The de-identification process involves:

Direct Identifier

It can identify an individual’s name, email, address, etc. 

Indirect Identifier

It can identify a person but is more critical for analyzing demographic information, socioeconomic details, etc. 

De-Identification Techniques 


This technique substitutes an exact value with a less specific value, such as changing the exact birth date to just a month or year. 


It substitutes direct or indirect identifiers with temporary but unique IDs or codes. 


This ensures that at least a “K” number of individuals have the same set of values. 


Names are omitted from the datasets.


Values are removed from the dataset or replaced with similar indicative information.


Values are exchanged between individuals. For instance, Jonathan swaps his salary with Wilbur while leaving the aggregate value of the dataset field valid.


Individuals with ages similar to 15, 16 and 17 group their ages together, representing each age as the mean of that group (i.e., 16 years old for everyone aged 16 and 17).

Noise Addition

This refers to the generation and addition of a new value to an original variable with mean zero and positive variance. 

De-Identification Methods

The image shows two de-identification methods Safe harbor and expert determination

The US HIPAA of 1996 specifies different methods for data de-identification. 

Expert Determination

It applies scientific and statistical principles to data to reduce the risk of re-identification. Therefore this method is the most flexible as it can be customized to each use case. Expert determination is a manual process that requires the involvement of a human statistical expert, which makes it expensive at scale. However, by using quantitative methods to reduce the risk of identification, expert determination enables data generalization and automation. 

Safe Harbor

The US Department of HHS developed this method of data de-identification. However, it requires the removal of 18 types of identifiers to ensure the information cannot be linked to a specific individual. 

  • Name 
  • Date of birth 
  • Phone number 
  • Street address 
  • Fax number 
  • Social Security Number 
  • Email address 
  • Bank account number 
  • Medical record number 
  • Health plan beneficiary number
  • Business license number 
  • Vehicle registration number 
  • Web URL 
  • Device serial number 
  • Internet Protocol (IP) address 
  • Passport or driver’s license photo 
  • Biometric identifier 
  • Any unique ID number 

According to HIPAA, these identifiers are considered PHI, which means their disclosure and usage are limited. This is why they need data de-identification. This method is simple and cost-effective, but it is not suitable for every use case. In many cases, it is overly restrictive which makes the data unusable. In others, it is overly permissive, leaving multiple direct identifiers unsecured. 

When De-Identification Is Not Required?

All qualitative data doesn’t need to be de-identified. 

  • When interviewers conduct ‘on the record’ interviews, they can share the notes or transcripts using the respondent’s name. This might often be the case for elites accustomed to journalistic interviews. However, in some countries, interviewers expect interviewees to have the chance to review the written record of the interaction before they publish or share it.
  • Data that is already part of the public record. For example, public statements by politicians that don’t need to be de-identified. 

Difference Between Data Masking And De-Identification 

The concepts of de-identification and data masking are interchangeable. However, it is crucial to understand what data must be de-identified. Let’s know their difference:

Category Data Masking De-Identification
Main Objective It protects sensitive data by creating a fake but realistic version of it. This is called a masked version of the data. It protects people’s confidentiality. Also known as anonymization, which is a critical part of sharing data for other purposes like research.
Use in Industries Production environment, Healthcare, IoT Law, Risk management, Healthcare, Online shopping
What It Can Identify Personally identifiable information (PII), Protected health information (PHI), Payment card information (PCI-DSS), and Intellectual property (ITAR) It identifies direct and indirect like names, addresses, and social security numbers, age, occupation, and postcode
Reasons To Use Meeting data privacy regulations, reducing security risk. Sharing data with authorized users Organizations require it to de-identify or destroy personal information, help to minimize the risk of privacy breaches, protect the privacy of individual's sensitive data

Challenges of Data De-identification

These are the challenges of data de-identification

Potential for Re-identification

No single de-identification method is foolproof. Moreover, each has its potential risk of re-identification, especially in smaller datasets.

Evolving technologies

Growing technologies like AI and machine learning can potentially re-identify patient information. Therefore, the challenge exists in privacy protections.

Privacy Protection Measures

Advanced privacy-enhancing technologies are required to ensure data remains de-identified. These include algorithms, PETs for augmentation, and other aspects that add complexity to the de-identification process. With this in mind, privacy measures need to be reconsidered.

Complexity of Healthcare Data

Healthcare data is complex and interconnected. Therefore, we must advance the de-identification protocols to handle complexities in the datasets while ensuring anonymity.

Maintaining Data Integrity

Data de-identification processes can introduce errors or inconsistencies in the data. However, applying robust governance practices can ensure the integrity and accuracy of de-identified datasets.

Data Utility and Privacy

It is important to maintain the right balance between data utility and privacy. To be specific, overly aggressive data de-identification can strip away valuable details, hampering the effectiveness of analytics.

Importance And Benefits of Data De-identification

Protects Confidentiality

De-identification protects an individual’s privacy by removing all the personal information connected to medical records or reports. However, it removes name, address, phone number, and social security number from the data, making it anonymous. As a result, the personal information is protected, anyone can use this data for research and analytics. 

Drives Medical Advancements

De-identifying allows the researchers to analyze a vast dataset to identify trends and patterns in diseases, drug efficacy, and treatment results. Therefore, it can bring breakthroughs in personalized medicine and targeted therapies. It helps to improve disease prevention strategies in the healthcare industry.

Secure Data Sharing

Collaboration plays a crucial role in medical research and its progress. In addition, de-identifying helps to secure data sharing between hospitals, and pharmaceutical companies. In short, it is crucial for developing better healthcare solutions.

Improve Patient Privacy

Data breaches can expose sensitive information. As a result, de-identification helps reduce the risk of data breaches by removing all the crucial identifiers from medical records. However, it also helps build trust and encourages participation in research initiatives.

Data Handling

De-identification aligns with ethical data handling practices and legal obligations. In addition, by implementing the measures, organizations are dedicated to striking an honest balance between data utility and personal privacy preservation. 

Simplify Regulatory Compliance

HIPAA and other data privacy laws regulate how an individual’s data is used. However, data de-identification falls outside the scope of these regulations, making it easier for healthcare providers to comply.

How AnnotationBox Can Help You With Data De-Identification?

Annotation Box is a leading data de-identification service provider, serving for years. In addition, our data scientists are aware of the latest data de-identification techniques and regulations. They will choose the right method for your dataset. 

However, we guarantee to deliver the best-in-class annotation services to train and develop machine learning (ML) and deep learning models. As one of the leading providers, we have served our clients with excellence, including everything from timely delivery and scalable data solutions while maintaining privacy and data security. 

Furthermore, the experts ensure the resulting data complies with relevant guidelines and regulations. Come work with us to keep the organization in compliance with HIPAA guidelines.

Shrey Agarwal