What is Data Anonymization?

Data anonymization play a critical role in protecting personal privacy by preventing the exposure, and exploitation, of people’s sensitive information. With the ever-increasing amounts of data being collected and stored, the risk that personal information could be accessed and misused – without someone’s knowledge or consent – is greater than ever. When personal information is violated, not only is it a breach of security for the organization, but, more importantly, a breach of trust for the customer or consumer. Such attacks can lead to wide-ranging privacy violations, including breach of contract, discrimination, and identity theft. 

By hiding or deleting the PII from datasets, data anonymization severely limits the ability of unauthorized users to access, or use, personal information. In addition to preventing privacy breaches, and protecting the rights of the individual, data anonymization enables organizations to comply with data privacy regulations – like APPI, CPRA, DCIA, GDPR, HIPAA, PDP, SOX, and more – which require companies to take preventative measures to protect an individual's confidential data. 

Just as important, even after data is anonymized, it can still be used for analysis purposes, business insights, decision-making, and research – without ever revealing anyone’s personal information. 

There are 5 key data anonymization techniques, including: 

1. K Anonymity
   K Anonymity ensures that no 1 person’s information can be distinguished from at least "K-1" other people in the same dataset. In other words, for any given record, there are at least K other records in the dataset with identical values for all identifying attributes.
   
   For example, if a dataset contains personal information such as names, addresses, and social security numbers, and K is set to 3, then no individual's information can be distinguished from at least 2 others in the dataset. This means that hackers won’t be able to identify a specific person within the dataset just by looking at the values of the identifying attributes – because there are at least 2 other people in the dataset with exactly the same values.
   
   K Anonymity can never guarantee 100% privacy protection, because as the value of K increases, the risk of re-identification decreases – but is never completely eliminated. Additionally, this data anonymization technique doesn't consider any external factors in identifying someone, so even when a dataset is K-anonymous, it can still be combined with other data sources to re-identify a specific person.
   
   
2. L Diversity
   L Diversity, which assures that no single person’s information can be distinguished from at least L other individuals in the dataset based on a sensitive attribute, is an extension of K Anonymity. But where K Anonymity ensures that no individual's information can be distinguished from at least K-1 others in the dataset, L Diversity protects sensitive, as well as general, attributes. For example, if a dataset contains sensitive attributes like a medical condition or prescription drugs, there should be at least L people in that dataset for any specific value of the sensitive attribute, in order not to identify a specific person.
   
   Like K Anonymity, L Diversity doesn't guarantee full privacy protection, for the same reasons cited in the previous section. And L Diversity is more difficult to implement than K Anonymity, because not only does it have to identify and protect sensitive attributes, it can only work when at least L distinct values for each of those attributes are present in the dataset.
   
   
3. T Closeness
   T Closeness contributes to the effectiveness of the K Anonymity / L Diversity combination by assuring that the distribution of the sensitive attributes in the dataset matches that of the target population, as closely as possible. For example, if a given dataset contains not only PII, but also sensitive attributes like income, T Closeness ensures that the distribution of income in the dataset is very close to that of the target population. That way, the income value doesn’t reveal any information about a particular person.
   
   Like K Anonymity and L Diversity, T Closeness can’t ensure complete privacy protection, for the same reasons cited above. And T Closeness is even harder to implement than K Anonymity or L Diversity, because not only does it have to identify and protect sensitive attributes, it can only be effective when the distribution of the sensitive attributes in the dataset is similar to that of the population.
   
   
4. Differential Privacy
   Differential Privacy, which adds random noise to the data in order to render it unidentifiable, is a mathematical framework used in data analysis, reporting, and visualization that seeks to balance the privacy risk of a given dataset vs its utility. It makes use of various randomization techniques, such as perturbation and sampling. A privacy protection level parameter, known as epsilon (ε), controls the amount of noise added to the data. The smaller the value of epsilon, the greater the noise level required.
   
   Differential Privacy can make the data less accurate, so it's important to strike the right balance between privacy protection and utility. And because there's always a small probability of re-identification (controlled by the privacy parameter), it can’t assure complete protection.
   
   
5. Randomized Response
   Randomized Response is a survey technique that works by randomly deciding whether a question is answered honestly, or given a pre-determined Yes/No response. It allows people to answer truthfully to sensitive questions, without revealing their actual responses. This is accomplished by introducing a level of randomness into the survey process, in order to keep the survey administrators from knowing the true response.
   
   In a survey about drug use, for instance, one of the questions might be, "Have you ever used illegal drugs?" This technique randomly assigns each respondent to either respond honestly, or give a pre-determined Yes response with a certain probability (say 0.5). The randomized response technique can be combined with other survey methods, such as anonymous surveys and self-administered surveys, to further protect the privacy of the respondents.
   
   As a probabilistic concept, randomized response can't deliver comprehensive privacy protection, because reidentification is possible, however remotely.

Below is a summary of the pros and cons of data anonymization:

Here's a list of data anonymization use cases, broken down by industry sector:

Healthcare 
The healthcare industry uses data anonymization to protect the privacy of patients, while permitting their data to be used for legitimate purposes such as analysis, reporting, and research. In healthcare, data anonymization is used to secure medical histories, personal information, and treatment details.

For example, data anonymization might be used for studies evaluating the efficacy of certain drug treatments, or to identify trends in disease outbreaks, without exposing patient PHI (Protected Health Information). It’s also used to comply with data privacy laws, such as the Health Insurance Portability and Accountability Act (HIPAA). 

Financial Services 
Financial services companies, such as banks, brokerages, and insurance companies, employ data anonymization to protect sensitive information such as financial histories, PII, and transaction information. Financial institutions can share and use anonymized data for research, analysis, and reporting, without compromising client privacy.

For instance, data anonymization is used to identify fraud patterns, or to test the effectiveness of marketing campaigns, without exposing any identifiable information. It’s also used to comply with data privacy laws, such as PCE DSS, which protects payment details, the US Securities and Exchange Commission (SEC), and the Financial Industry Regulatory Authority (FINRA), which require financial institutions to secure the personal and sensitive information of their clients. 

Telecommunications 
Telco and media companies use data anonymization to protect sensitive information, such as call/message logs, location details, and PII. They share and use anonymized data for reporting, research, and analysis, without the fear of compromising customer privacy.

For example, data anonymization can be used to enhance network performance, gauge the effectiveness of marketing campaigns, or identify usage patterns – without exposing any identifiable data. It's also used to comply with data protection regulations, such as the US Federal Communications Commission (FCC) whose privacy rules protect broadband consumers by granting them choice, transparency and security for their personal data. 

Government 
Local and national governments employ data anonymization to protect sensitive data, such as citizen information, voting records, and tax records. They anonymize data for analysis, research, and reporting, with no risk to the privacy of their citizens.

For instance, data anonymization can be used to conduct population-based studies, to measure the effectiveness of public policies, or to understand trends in crime or poverty – without exposing citizen-identifiable information. It's also used to comply with data protection regulations, such as the General Data Protection Regulation (GDPR), and the California Privacy Rights Act (CPRA), which require government agencies to protect the personal and sensitive information of their citizens. 

The 4 main challenges to data anonymization are: 

1. Preempting re-identification 

   Despite all the efforts expended on data de-identification, the risk of linking anonymized data to a single person always exists.
   
   One of the main ways to determine the identity of specific individuals is through a linkage attack, which cross-references anonymized data with publicly available records. A linkage attack could be carried out by combining anonymized bank information with data from a voter registration database.
   
   Another way to re-identify individuals is via an inference attack, which uses their attributes, such as age and gender, to infer their identity. One example of an inference attack is to cross-reference location, and browsing histories, to infer their identity.
   
   There have been several advancements in the re-identification of anonymized data over the years. Today, machine learning models can be used to analyze patterns found in anonymized datasets. Advanced data mining and data linkage methods make it easier to combine multiple datasets to perform re-identification.
   
   

2. Striking the right balance between privacy and utility 

   Balancing privacy and utility is a major challenge for those involved in data anonymization. A risk-based approach helps ensure that the level of anonymization is directly proportional to the level of risk associated with the data.
   
   For example, data containing medical records probably requires a higher level of anonymization than data containing demographic information. Other approaches include differential privacy, as described above, or the use of AI/ML-based generative models (like GANs).
    

3. Developing international standards and regulations 

   As data becomes more and more important to businesses and researchers, the need for consistent and effective governance of data anonymization has become acute. A number of different data anonymization standards and regulations currently exist, each with its own strengths and weaknesses.
   
   For example, while GDPR provides strong protection for personal data, it makes data sharing (for business and research purposes) quite difficult. One potential solution is to develop a single standard for data anonymization that protects personal data while also accommodating different data types, legal requirements, and use cases.
    

4. Integrating with AI and ML models 

   The integration of Artificial Intelligence (AI) and Machine Learning (ML) is a key challenge in data anonymization. The most obvious approach is to incorporate AI/ML in the data anonymization process, with methods like Generative Adversarial Networks (GANs) which generate fake data that preserves the statistical properties of the original information while removing PII.
   
   A possible future direction is to use AI/ML for data de-anonymization, including methods for reidentification and linkage. Since data de-anonymization is a real threat to data privacy, AI/ML can help identifying and fix any vulnerabilities in the process.

Future research in data anonymization might focus on:

• Developing more secure and robust methods, such as homomorphic encryption, which allows sensitive data to be processed without exposing it in plaintext
• Improving efficiency and scalability, particularly for large datasets
• Integrating AI/ML using generative models and clustering-based techniques, which group similar records together in a dataset, and then apply privacy protection methods to the aggregated data in each cluster
• Optimizing the privacy vs utility ratio
• Investigating blockchain technology, which provides a decentralized, tamper-proof transaction ledger, for secure data sharing
• Collaborating across different domains, without sharing any raw data in a federating learning approach
• Examining differential privacy in terms of time-series data with temporal dependencies

With entity-based data masking technology, data teams can anonymize data more quickly and efficiently. It integrates and organizes fragmented data from multiple source systems according to data schemas – where each schema corresponds to a business entity (such as a customer, vendor, or order). 

Data privacy regulations are driving enterprises to anonymize the data of their important business entities (customer, suppliers, orders, invoices, etc.).  

This paper covered the definitions of, and need for, data anonymization, listing its types, techniques, applications, challenges, and future research in the field. 

It concluded by presenting a business entity approach to data anonymization, delivering unprecedented performance, scalability and cost-effectiveness.