Data has evolved into a vital tool for companies looking to grow and outperform their competitors. Companies examine data to better understand consumer behaviour, challenges with supply and demand in the market, price trends, etc. However, when handling sensitive customer data, privacy and security become a concern, especially in light of the General Data Protection Regulation (GDPR) act.
Companies implement different methods to protect sensitive customer data from breaches and other risks. This is to ensure they are in compliance with GDPR and other regulations like HIPPA and CCPA. Data anonymization is one such technique employed by a wide range of organizations today.
What’s data anonymization?
In simple terms, data anonymization is securing sensitive user information through different processes. This is achieved by encrypting or removing personal identifiers related to individuals and their associated data. For instance, let’s say you have a large dataset of thousands of your website users. You can strip the dataset of any information that could be used to identify the users, including IP addresses and geolocations, and then give the dataset to an analyst. The analyst can then study the database for research without risking privacy breach.
Interestingly, businesses can collect anonymized data without requiring individual consent. This is while complying with GDPR regulations. So, they can gather, store, and use the data for as long as necessary, as long as the identifiers are removed.
Even as anonymization is widely used today, it’s important to note that hackers can use some advanced techniques to breach this protection by retracing the anonymization process. This can happen when cyber attackers cross-reference public data sources to gain access to personal information. So, adequate precautions must be taken to increase the effectiveness of data anonymization.
What are the advantages of data anonymization?
The following are a few key advantages of data anonymization:
Prevents misuse of data
The biggest benefit of data anonymization is of course, that it helps prevent misuse or exposure of sensitive data by cyber criminals. It also safeguards against insider exploitation risks (as per some reports, insiders are responsible for up to 1 in 5 cases of security incidents).
Accurate data enables more effective user targeting through specialized services. Data anonymization increases uniformity and enhances governance. Additionally, you may guarantee the privacy of critical user data, which has significant value to organizations.
Acts as a damage control measure
No system is entirely foolproof, so a business must always be prepared for a possible data breach. However, even if breach takes place and if that business is using data anonymization, it can protect sensitive data from compromise because the data won’t be of much use to the attacker anyway. So, in a way, the technique also helps limit damages from data loss in a database breach.
GDPR necessitates the pseudonymization/anonymization of the data of individuals living in the EU. Similarly, there are regulations such as HIPAA in the US that require organizations handling PII data or personal data of users to deploy adequate data protection measures. With data anonymization, companies can protect customer data and avoid hefty fines and loss of business.
Enhances business performance
Since anonymized data can be easily analyzed and used without breaching leading regulations, organizations can leverage consumer data to gain insights and create better marketing strategies, thus improving business performance.
What are the different techniques of data anonymization?
Data anonymization can be achieved through a variety of techniques. These are:
Data obscuring involves changing data values to enhance security. This is done by modifying the data in a database using methods such as character substitution, encryption, or character shuffling. For instance, characters may be replaced by symbols like “*” to make the data harder to reverse engineer or identify.
In this approach, private identifiers in a data set are replaced with false identifiers or pseudonyms. So, the accuracy of data is maintained while ensuring privacy. This approach enables modified data to be used for creation, training, testing, and analysis while still protecting the sensitive information.
Data abstraction entails deliberately removing some data to reduce its chances of identification. This can be achieved by turning the data into a series of ranges or a large area with reasonable boundaries. For example, in an address database, house numbers may be deleted, but the street names can be left. The goal is to remove some of the identifiers while preserving data accuracy.
Synthetic data generation
Synthetic data generation refers to the creation of algorithmically generated information that has no relation to any real-life scenario. This is used to construct artificial datasets instead of modifying or using the original dataset, thus avoiding any privacy or protection concerns.
The synthetic data generation method involves constructing mathematical models based on patterns in the original dataset. This is done using statistical methods such as standard deviations, linear regression, or medians to produce synthetic results.
Forward-thinking organizations have to think of all kinds of things along with sales and products to grow business and improve their brand positioning. Since they deal with customer data on a large scale, they have to not only leverage this data but also protect it. Thanks to advanced techniques like data anonymization, they can easily protect sensitive databases from prying eyes and instill trust in their customers and stakeholders. That said, it’s important to use select the best privacy enhancing tools and technologies so there is no margin of error.