- Insights
- Cryptocurrencies
- Stocks
- White Papers
- Industry
- Geography
With the help of data anonymization, the organizations can safeguard their data without compromising their customers.
Data is collected directly or indirectly daily by the organizations. Be it during the time the employee logs in the homepage, or while the customer adds some products to their e-retail cart. Due to its frequent collection, the data collected is not segmented as useful or stale data. Despite the data scientists cleaning the data regularly, for its reliable use, the probability of data infringement is high, which can cause some severe damage to the organizations.
The wave of data transmission, at this point, builds a rippling effect across the organizational setup, where data is transmitted, thus creating an interconnecting and igniting a fibrous network of neurons.
According to a survey conducted by IDC, by the year 2018, human activity had generated 18 Zettabytes of data. It also predicted the generation of 175 zettabytes by the year 2025.
However, the generation of this massive amount of data regularly makes organizations prone to malicious activities. That's why organizations such as General Data Protection Regulation (GDPR), formulated in the year 2018 by the European Union, require the companies to anonymize and remove the personal data that are suspected of falling prey for malicious activities.
A study by Privacy affairs states that, despite the integration of stringent measures like GDPR for data protection, most of the companies under the EU, are unable to tag along with the guidelines of GDPR. A total fine of 158 million pounds has been issued in 340 cases for the companies who failed to abide by the rules. France became the country with the highest fines accounting to 51 million pounds, and Spain became the country having the most number of fines.
Another research survey by PCIPal, a payments compliance company, across the Eurozone, states that 47% of the European customers sever ties from the business following a data breach. While this alarming, many organizations have been subjected to the lawsuits following incidents of Data breach.
In June 2020, more than 10,000 people joined the lawsuit worth 183.4 million UK pounds, against EasyJet, an airlines agency, for a massive data breach of 9 million people. Other major organizations like Yahoo, Walmart, and Salesforce, have also faced the lawsuit because of the failure to retain data from a possible breach.
Thus data breach can have significant implications that not only cause economic loss amongst the organizations but hampers the trust amongst their customers. Hence, to counter this major challenge involving data, data anonymization is an imperative process.
Data anonymization is the process of protecting private and sensitive data, from the possible malware, by encrypting or erasing the information of the users, which connects them to the data. This can be done by applying the secured Personally Identifiable Information (PII), which enables the organizations to build an information security environment for masking or anonymizing the information of the user or the source, without losing the business value.
Many organizations use PII for identifying the people whose data has been stored, processed, and managed. PII is legislation in many countries like the USA, Australia, European Union, Australia, Newzealand, and Canada.
The data anonymization can be done with the help of the following steps, which are described down below.
Since the data is exposed to many networks simultaneously, masking it by making altered changes in its value would prevent the source from being detected. These alternations in value can be in the form of modifying database techniques such as shuffling of characters, encryption, or substitution of a character or a word.
This technique, also known as "Swap and replace," helps replicate the data source for testing, or training purposes, while maintaining the original user security.
As the name suggests, this technique involves data management by replacing the original user name with a pseudonym or swapping the personal identity with the fake identifiers. This helps in data training, data security, data training, and data management by preserving the accuracy and integrity of the data.
Data Generalisation involves removal of the precision from the data set, to make it less identifiable. However, this technique can weaken the statistical accuracy of data but helps in securing the privacy of the people it represents. Google uses Data Generalization before sharing its PII across various services. This can be elucidated in the following manner: removing the house numbers from a particular address or area so that statistical accuracy can be maintained, without breaching the security.
Also known as Data Permutation or Shuffing, it applies the principle of Permutation and combination. This technique involves shuffling the attributes of various dataset values across different rows and columns so that the data can be available, without disclosing the name of the user.
This is done by slightly modifying the original dataset by multiplication, subtraction, or adding a numerical value.
This technique utilizes the creation of artificial datasets instead of altering the original ones, without risking privacy or security. The statistical model is created based on the patterns from the original dataset.
Any of the above techniques can be applied while anonymizing the data. Without data anonymization, the companies and organizations are at a higher risk of being slapped with a lawsuit, as mentioned in the above examples.
