Updated March 10, 2023
Introduction to Data Anonymization
Data anonymization is described as the process where the personal information is irreversible changed in a method where no one can be identified indirectly or directly by a single data controller or collaborate with third parties. It may allow to transfer of the data across any confined areas and shared between any department or agencies to limit the risk of any unintended closures. In a few circumstances, it allows post anonymization and analytic evaluation. The importance, limitations, and advantages of data anonymization are explained in this article.
What is Data Anonymization?
It is the method of safeguarding the data or any sensitive data by encryption or erasing the identifiers which are related to any individual to extract the saved data. For instance, the user can execute personal identifying information such as security numbers, social names, and the addresses via data anonymization techniques to retain the information and maintain the source as anonymous.
Although the user erases the identifier data, the intruders can apply de-anonymization techniques to retrace the process. As the data is traveled via multiple resources, little information is open to the public, and cross-reference can be made with the de-anonymization method to extract the data source and personal information.
The GDPR policies outline the constrained set of rules to save the user information and create a transparent view of the public. Though the GDPR is stringent, it enables multiple enterprises to gather the anonymized data without any consent and deploy it for any purpose and save it for an indeterminate time. It remains the same or unaltered as long as the organizations eliminate all the identifiers from that information.
Importance of Data Anonymization
The data anonymization supports the enterprise to manage the PII private by data masking the sensitive attributes even as they extract business values from the client support, test data, analytic insights, the purpose of the supplier to outsource, and more. Moreover, the difficult requirement of GDPR offers a standard benchmark of the data type to save or regardless of whether an enterprise processor stores the PII about the details of EU citizens. The GDPR describes the personal data as any data associated with an identifiable or already identified data subject.
This information comprises of basic identifiable data like address, name, id numbers. The source web data includes IP address, cookie information, location, and RFID tags. It also includes biometric data, genetic information, political opinions, ethnic and racial information, sexual orientations, and health information. When the CCPA is launched, it will cover all the wider class of personal information. The enterprise is responsible for protecting the data to identify, relate, describe, and link the information indirectly or directly with particular clients or household functions and conduct business with some allocations.
Depending on the business, the involved data type can be the identification of vehicle numbers to streaming the data from mobile towers, or it can also be enabled in IoT household smart applications. Many businesses must comply with regulations of industry-specified. An insurance company, the Independence health group, is the best example to show how an organization can become successful in applying the data anonymization methods concerning healthcare. This health group is registered under HIPAA, which strictly follows the regulations of healthcare information in America.
The organization must also save the data where it has 8.3 million people insured in it to avoid any trust issues. Anyway, the insured people also need to join hands with third party data processing partners and enable the outsourced developer and in-house employees to test the applications on the associated data. This company uses a dynamic data masking process to test and build high-quality applications to process the data without any risk from unauthorized access. The wide array of data comprises birthdates, names, and social security values to identify the person and billing record made with dynamic data masking.
Advantages and Disadvantages of Data Anonymization
It is the method to show how the company enforces and finds its responsibility to safeguard sensitive data, personal, confidential information in a circumstance that has the complex standards of data privacy, and it also depends on the location of global clients. Clients who trust sensitive information to industries will consider any breaches on the trust sites and move their business in another direction to get a better result. If one set of industry surveys shows that 85% of clients don’t ready to make any dealing with a business that has any security issues. Only 15% believe that most of the organization manages the PII data with more responsibility.
Improvement in data security guides the data anonymization process by the data masking methods and classifies the sensitive data. It offers multiple information methods and ensures the class scalability and efficacy of the enterprise. The data masking methods and data anonymization methods are a section to traditional security solutions that safeguards and acts as a strong firewall to the data where it stays and gets processed, let it be on cloud or on-premise, or it can be in hybrid environments. It also gives maximum security and visibility to the IT teams on how the information is processed, accessed, used, deployed, and moved around the enterprises.
The process of GDPR demands the websites have some consent to the users whenever they enter personal information like device ID, IP address, and cookies. Collection of anonymous information and deletion of identifiers from any database to restrict the capability to derive the values and indulge it to the data of the user. For example, this anonymized information is not fit to compute the marketing efforts or make any personalization on user experience.
Data anonymization is implied not to avoid any risking issues but also to enhance data quality and data governance. With trusted and clean information, the user can optimize the application to protect the big data privacy and analytics and accelerate the cloud’s workloads, which regulates the digital data by opening up secured data for implement in developing new business values.
This is a guide to Data Anonymization. Here we discuss the Importance of Data Anonymization along with the Advantages and Disadvantages. You may also have a look at the following articles to learn more –