Guide: Data Classification

The Safe Harbor Method of De-Identification

Data is a crucial asset for any organization. Data generated, acquired, saved, and exchanged are all essential to the growth and success of a company. Thus, protecting a company’s data against internal or external corruption and unauthorized access helps prevent financial loss, reputational damage, customer trust degradation, and brand erosion.

Yet, keeping data locked in a digital stronghold so secured that it is inaccessible is useless. Therefore, one of the best defenses in your data security arsenal is the method of de-identification.

Specifically, the importance of the Safe Harbor method of De-Identification gets highlighted.

This article will discuss the following:

What is Data De-identification?

When it comes to data de-identification, it is a type of dynamic data masking that refers to the process of removing the link between data and the person with whom you already connected the data. Essentially, this is the removal or transformation of personally identifiable information. The data de-identification procedure makes it much easier to reuse and exchange information with third parties after personal identifiers have been deleted or changed.

 

Data de-identification gets expressly mandated by HIPAA, so most people link the data de-identification procedure with medical data and medical records. On the other hand, HIPAA De-identification is vital for enterprises or agencies that desire or need to mask identities under different standards, such as the CCPA and CPRA, or even the GDPR.

What is the Safe Harbor Method of De-identification?

The HIPAA Privacy Rule includes the HIPAA Safe Harbor provision. The HIPAA Privacy Rule restricts how protected health information, or PHI, can be used and disclosed. As a result, PHI safe harbor or the development of de-identified PHI is the HIPAA Safe Harbor method of de-identification. The elimination of specific information about a patient that you can use singularly or other information to identify that patient is known as de-identification of patient data.

 

In a nutshell, the HIPAA safe harbor de-identification method is the process of removing the patient’s and the patient’s relatives, household members, and employers’ designated identifiers.

 

The HIPAA safe harbor de-identification process is complete if the covered organization has no full information. You may use the remaining data to identify the patient. In other words, HIPAA de-identified information is PHI stripped of identifiers.

The Value of De-identification

There are several advantages to performing de-identification under HIPAA.

 

As previously stated, de-identified information is PHI stripped of identifiers. Since the information no longer gets considered identifying data under the safe harbor method HIPAA, you may not be obligated to report breaches or data leaks. This de-identified process can also help protect people by limiting their risk exposure.

 

For example, de-identified PHI can get shared with third parties via secure data licensing. The Safe Harbor method HIPAA De-Identification of data can also enable researchers to offer public health warnings without revealing PHI and other sensitive data sets. Researchers and policymakers can discover trends and potential red flags by aggregating HIPAA de-identified information and taking appropriate steps to limit dangers to the general public through HIPAA de-identification experts.

 

In the health care sector, data de-identification has shown to be extremely useful, and it is at the core of research that has led to advancements and breakthroughs that have improved patient care. Today, it continues to prove its value across other industries.

Identifiers to be De-Identified

You can accurately utilize specific data to identify a person individually or in combination. The following data elements can get used to uniquely identify a person and, as a result, must be de-identified when using the safe harbor method:

 

  • Names
  • Account numbers
  • Biometric identifiers
  • Certificate and License numbers
  • Dates, such as discharge dates, except the year
  • Device identifiers and serial numbers
  • Email addresses
  • Fax numbers
  • Full face photos and comparable images
  • Geographic data, including geographic units, formed
  • Health plan beneficiary numbers
  • Internet protocol addresses
  • Medical record numbers
  • Social Security numbers
  • Telephone numbers
  • Vehicle identifiers and serial numbers, including license plates
  • Web URLs
  • Any unique identifying number characteristic or code

 

Any identifiers can classify health information as protected by any identifiers, limiting its use and disclosure and necessitating its de-identification.

Safe Harbor Method of De-identification vs. Data Masking

The concepts of data masking and de-identification are often interchangeable, but what matters is that you know what data has to be de-identified and why and the best strategy for the job.

 

De-identification removes sensitive data pieces from a person’s identity to protect their privacy or comply with regulations.

 

On the other hand, the technique of replacing sensitive information with realistic replacement data such that the data cannot get used in identifying an individual is known as data masking directly. Data masking is a broad phrase that encompasses several techniques such as shuffling, encrypting, and hashing.

 

Like the other terms, Anonymization gets used to create data that you cannot trace back to a specific person. Data masking has become synonymous with the same function because of the range of algorithms used to de-identify direct and indirect identifiers, such as k-anonymity.

Summary

As the world is thriving in a data-driven space, data security must be on top of every company’s priority.

 

Data de-identification is a potent way to uphold data security while remaining compliant with various data security regulations. Although seemingly complicated to implement and monitor, it does not have to be.

Data Classification with Satori

Satori provides a different approach to data classification. With Satori, data is continuously discovered and classified, instead of performing ad-hoc scans. 

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