Data anonymization is a technique used to protect sensitive or private information by removing or masking any personal identification information that can be used to identify an individual. Yash Mehta of Expersight shares data anonymization tools and use cases that enterprises can employ to protect personal and sensitive data better.
As the amount of personal data collected and stored digitally increases, consumers are also at more risk of their data being accessed by unauthorized entities. This requires enterprises to handle and manage such sensitive information more carefully and comply with regulations on personal data that are becoming more stringent.
The Growth of Data Masking
The surge in the adoption of advanced technologies such as artificial intelligence, large language models, and growing adoption of cloud-based services, especially by different scale enterprises and mitigating the risk of data breaching, can be considered as attributable factors to the growth of the data masking market. According to a report by ExpersightOpens a new window , the data masking (a technique for data anonymization) market was valued at USD 801 Million in 2023 and is projected to reach USD 1274.8 Million by 2028, growing at a CAGR of 11.54% from 2023 to 2028.
Additionally, enterprises often ignore Insider threats, wherein employees can intentionally or unintentionally access and disclose sensitive data. For example, an employee might accidentally send an email containing sensitive data to the wrong recipient. Such unprecedented incidents are tackled well with Data Anonymization.
Not all the data stored and processed requires data anonymization; businesses should consider anonymizing any data that can be used to identify individuals or groups, especially if the data is sensitive or confidential. This includes personally identifiable information (PII) such as names, addresses, social security numbers, driver’s license numbers, passport numbers, and other unique identifiers. What is defined as sensitive data is a subjective idea that changes according to the individual and the industry, most compliance standards and organizational policies agree that Personally Identifiable Information (PII) should be treated as sensitive data and managed safely. A broad consensus includes the following as PII information.
- Full name
- Social security number (SSN) or national ID number
- Date of birth
- Home address
- Email address
- Phone number
- Driver’s license number
- Passport number
- Financial account numbers (e.g., credit card numbers, bank account numbers)
- IP address
- Biometric data (e.g., fingerprints, facial recognition data)
- Medical information (e.g., health records, genetic data)
There might be more data elements that can be classified as sensitive data, or any information that can act as an identifier should also be anonymized. Businesses can take appropriate measures to safeguard PII from any misuse by using data masking tools.Â
This holistic view helps businesses to identify who has access to sensitive data, why, and what outcomes it has impacted. With automated data protection for high-risk data, their solution helps businesses to safeguard PII data and comply with data privacy regulations. This helps businesses to build trust with their customers and protect their reputation. Their universal data privacy framework provides a comprehensive solution for managing data privacy policies across disparate data sources and localities.
See More: 5 Best Practices To Implement for Data Privacy
Industries That Can Benefit From Data Anonymization Techniques
1. Healthcare: Data anonymization protects patients, their medical history, diagnosis, and treatment. Anonymization of critical medical or health information at the patient level, such as names, addresses, and other personal details, to prevent unauthorized access and privacy breaches must be safeguarded from unauthorized access or breaches.
In healthcare, clinical trials and other research studies require access to large amounts of data to identify health trends and patterns and successfully make evidence-based decisions. By anonymizing the data, researchers can access the information they need while ensuring that identifiable links between participants and their health information are removed from the data.
It is also essential to comply with health-specific data protection regulationsOpens a new window (WHO guidelines) such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States and even the General Data Protection Regulation (GDPR) in the European Union. These regulations set strict standards for healthcare organizations in collecting, using, and disclosing patient data and require and compel them to take appropriate measures to protect the privacy and confidentiality of patient information.
See More: The Benefits of Dark Data in the Healthcare Industry
2. Retail: data is collected about customer purchases, such as transaction history, product preferences, and payment information. Vast amounts of customer data, including their purchase history, preferences, and demographic information, are collected for various purposes, such as effective marketing campaigns or to gain insights into their sales performance or improve their business operations, such as optimizing inventory levels and improving customer service. Thus, Data anonymization is used on identifiable customer information that could potentially infringe on their privacy.
3. Finance: Financial businesses, banks or other financial institutions collect and process large amounts of sensitive financial data to offer highly customized services to clients and then create scalable business models. However, the finance industry is also highly vulnerable to cyber-attacks. Data anonymization can help prevent fraud and identity theft while allowing financial analysts to identify trends and patterns.Â
Therefore, data anonymization is critical in many industries where sensitive information is collected and analyzed to gain business insights and comply with regulations. It is also important in industries such as marketing and advertising, where large amounts of data are collected on consumer behavior. With anonymization, businesses can leverage data to extract insights without violating individuals’ privacy rights.
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