Table of Contents

    Table of Contents

    Do I Really Have to Anonymize Data?

    Amitai Richman

    Amitai Richman

    Product Marketing Director

    Companies anonymize data to ensure that private information remains that way. How important is it to anonymize data, and what happens if you don’t?  

    Table of Contents

    Why do Organizations Need to Anonymize Data? 
    What Does it Mean to Anonymize Data?
    Which Industries Should Anonymize Data?
    Anonymize Data Better and Faster with Business Entities

    Why do Organizations Need to Anonymize Data? 

    Nearly all enterprises collect Personally Identifiable Information (PII) as well as data that is considered sensitive. From names and addresses to credit card numbers and national identification numbers (like Social Security Numbers), this data is subject to increasingly more stringent privacy protection regulations such as CPRA, GDPR, HIPAA, PCI DSS, and many more. This means that if this data is exposed through a breach or other means (malicious or accidental), the organization that collected and stored it may be subject to massive fines and penalties that can literally reach millions of dollars – a huge liability. What’s more, companies that suffer leaks of PII are subject to harsh public and media responses – damaging brand equity and consumer trust, and ultimately negatively impacting revenues. 

    Aside from avoiding regulatory liability, organizations need to anonymize data to achieve consistency, improve governance, and facilitate digital transformation. When data is anonymized correctly, it is clean and accurate. This means it can be used by apps and services without compromising privacy. By enabling businesses to leverage their big data without violating privacy, data anonymization powers digital transformation and data-driven decision making. What’s more, companies that anonymize data reduce the risk of insider threats – protecting sensitive data from misuse or exploitation by employees, partners and third parties. 

    Collecting and storing PII may be risky, but it’s completely necessary. Companies need to store and access personal or sensitive information for operations, customer service, and marketing purposes. To enable this data to be stored safely, while still being accessible and usable, companies need to find ways to protect it. Perhaps the most common way is to anonymize data. 

    What Does it Mean to Anonymize Data? 

    When an organization decides to use data anonymization tools to anonymize data in a given dataset, they basically remove the identifiers from it. The reason for this is that identifiers – used separately or together – may allow hackers to retrieve private information about a specific individual from the data. And this would make the organization liable under law. 

    How are these identifiers removed? The 12 most common data masking techniques are: 

    1. Masking 
      Data masking tools anonymize data by altering values – for example, replacing characters or character strings with symbols like an asterisk (so the phone number 201-555-5555 would be stored as 20*-***-****). Data masking makes reverse engineering or detection impossible.

    2. Pseudonymization 
      Pseudonymization de-identifies sensitive data values by substituting fake identifiers or pseudonyms. For example, every instance of the name “Sam Smith” could be replaced with “John Q. Public,” or something similarly generic. Using this method, organizations can anonymize data while still retaining the integrity and statistical accuracy of the data. 

    3. Hashing
      The hashing method of data anonymization turns a key or string of characters into other values, which are then mapped using a function or algorithm so as to be discoverable without revealing the original data. This method is permanent – meaning that there’s no way to undo it. 

    4. Redaction 
      Considered one of the simplest ways to anonymize data, redaction removes or obscures sensitive values from datasets. This enables sharing with no concern for violation of privacy regulations. 

    5. Nulling 
      Similarly simple, nulling replaces sensitive data values in a dataset with a series of NULL values or attributes instead. 

    6. Encryption 
      When considering data masking vs encryption, the latter turns data into encrypted code that only authorized users with the decryption key can access. Article 32 of GDPR, for example, specifically recognizes encryption as a viable and acceptable measure to protect data security. 

    7. Swapping 
      Data swapping is also referred to as data shuffling or data permutation. It rearranges the attribute values of dataset, so that they no longer sync with the original values.  

    8. Generalization 
      By generalizing anonymized data, parts of a dataset are removed to make it less identifiable. But to retain data accuracy, only identifiers are removed. For example, in an address, the house numbers would be redacted, but not the street name. 

    9. Bucketing 
      Bucketing turns a particular distinguishing value – like a last name – and turns it into a generalized value, like <LASTNAME>.  

    10. Perturbation 
      Perturbation changes the original dataset slightly, by rounding off numbers or randomly adding noise. 

    11. Tokenization 
      Tokenization replaces sensitive data with values that are not sensitive values. For example, data tokenization tools would anonymize a bank account number by replacing it with a random string of characters. The tokenization of data can be reversible, when equipped with an encryption key to an offsite token vault. 

    12. Synthetic data generation
      Synthetic data generation, one of the most advanced methods of anonymizing data, uses an algorithm to generate fake data with no connection to the real data. It creates an artificial dataset using statistical models that are based on patterns in the original dataset. 

    Which Industries Should Anonymize Data? 

    While data anonymization software is suitable for almost any industry, there are some sectors that particularly need to anonymize data, notably: 

    • Financial services 
      By anonymizing sensitive data, financial services companies can better comply with industry-specific privacy regulations like PCI DSS. At the same time, they can make use of big data resources to offer more customized products to specific audience segments and enhance competitiveness in tough markets.

    • Healthcare
      Healthcare is subject to some of the most stringent regulations – HIPPAA in the US, GDPR and others in Europe, and the Data Protection Act (DPA) in the UK – just to name a few. Providers that anonymize data can conduct research effectively without compromising patient privacy or falling afoul of regulations.  

    • Energy
      The energy industry needs to collect detailed usage data to better provision their customers. By choosing to anonymize data, energy and utility companies can ensure continuity of service without violating privacy regulations.

    • Education 
      Educational technology is a valuable addition to the educator’s toolbox. But if solutions collect PII to track a student’s progress, masking data is a great way to avoid privacy issues. 

    Anonymize Data Better and Faster with Business Entities  

    To effectively anonymize data in the most technologically advanced way, organizations are adopting the entity-based data masking technology. A “business entity” is any element of the business itself, such as a customer, invoice, device, or facility. Data associated with each entity instance is stored in its own individually encrypted Micro-Database™. By basing data anonymization on business entities, organizations raise productivity without compromising on data compliance and customer privacy. 

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