Organizations generate and store a large amount of information across their departments, from personal data to purchasing behavior and location details. This information can be very valuable when carrying out research and development projects, however, it is of increasing concern to users, especially on the Internet.
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3. The advantages and limitations of anonymization techniques |
As a consequence, these days, guaranteed privacy requires data anonymization techniques, and sometimes even procedures for eliminating the possibility of reverse engineering for data retrieval. So much so, that in 2018 the EU GDPR made personal data removal mandatory for companies and organizations.
This article will discuss some of the most common personal data anonymization techniques that everyone should be aware of. From data masking and pseudonymization to techniques such as data disturbance or the use of synthetic data. This article will give you an overview of the different data anonymization techniques that are used to protect personal data privacy. Keep reading!
As the amount of personal data collected and stored digitally increases, the risk of that data falling into the wrong hands increases and can compromise personal data privacy and security. In addition, regulations on personal data are becoming more stringent, requiring companies and organizations to handle personal data more carefully.
Personal data anonymization is a technique that helps protect an individual's privacy and security by hiding their identity in the data collected. By anonymizing personal data, personally identifiable data is removed or modified, but usage-related data is retained. This allows companies and organizations to use the data without compromising the privacy and security of individuals.
Monolingual and multilingual anonymization techniques help companies and organizations comply with legislation and avoid fines related to data publication and disclosure.
Below, we propose a list of the main anonymization methods and their particular use in each scenario involving sensitive information, such as personal and banking details, passwords or home address data.
Data masking allows you to hide certain parts of data by placing random characters or other data in their place. In this way, substitution is used to alter key values, allowing the data to continue to be identified without revealing identity.
Alteration strategies are implemented, such as character shuffling, encryption, or character or term substitution. For example, a value character can be replaced by a symbol, and a person's name can be replaced by a number.
Data masking ensures that sensitive customer information is not available outside the production environment. One of its most widespread uses is in billing scenarios.
In this case, the card information is masked, changing part of the digits to an X. It should be used if you are looking to protect datasets that will not affect the performance of functions, such as personal identification or payment information.
While other anonymization techniques, such as data masking, ensure that anonymized datasets are difficult to retrieve, pseudonymization merely reduces the linking of personal data to the identity of the individual. It replaces private identifiers with false identifiers or pseudonyms, but maintains a specific identifier that allows access to the original data.
Data pseudonymization maintains the data's statistical accuracy and confidentiality. On the one hand, it complies with ethics and imposed legislation, and on the other, it continues to allow the modified data to be used for studies, research, statistics or other beneficial actions.
Pseudonymization prevents the identification chain from breaking, so that even if the data is dissociated, it is possible to achieve re-identification. It is usually found in the health field, where identifying data is separated from health data, preventing sensitive information from being traced.
Pseudonymization is useful, for example, for verifying specific and unique problems in a test environment. It is, therefore, often the only solution that allows applications to operate normally and perform the integrity of test scenarios.
Also known as data shuffling or permutation, data swapping involves changing the order or position of the elements of an ordered set.
This technique introduces a random distortion into a set of microdata, maintaining the detail and structure of the original information. Its main feature is, therefore, reordering the attribute values so that they are still present, but do not correspond to their original records.
In general, the data swapping approach is implemented by creating pairs of records with similar attributes and then swapping confidential or identifying data values between the pairs.
The process of mixing personal datasets to reorganize them causes them to no longer conform to the original information. It is commonly used in polls, where attributes (columns) that include recognizable values, such as date of birth, are changed.
Although synthetic data are technically not part of the anonymization tools, they are increasingly used when processing personal data so that their use does not interfere with the law.
Synthetic data refers to datasets created by an algorithm with no relation to existing events or reality. Statistical models powered by artificial intelligence are able to create synthetic prototypes from the original datasets.
The synthetic data method involves the construction of mathematical models based on patterns contained in the original dataset. Relying on deep learning, it uses methods such as standard deviations, linear regression or medians, among others, to produce synthetic results.
Synthetic data offer highly accurate simulation environments, allowing datasets to be used to gain strategic insights on the future of, for example, markets, without putting users' privacy at risk.
They are used to construct artificial datasets instead of modifying or using the original dataset and compromising privacy. Some experts consider this to be simpler than making modifications to the original datasets.
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Data perturbation is a data security technique that adds "noise" to databases, advocating the confidentiality of individual records. This method of anonymizing datasets is applicable to numerical data entries by altering the datasets with a specific value and operation.
This technique slightly changes the initial dataset by using rounding and random noise methods. The values used must always be proportional to the perturbation used.
Data perturbation can add an amount to all numeric values in your database, or use a given number as the basis of your operation, dividing all numeric values by it.
It is important to carefully select the base used to modify the original values, because if the base is too small, the data will not be sufficiently anonymized, and if it is too large, the data may not be recognized and its value may not be extracted.
Data generalization is the process of creating a broader categorization of the data in a database, creating a more general picture of the trends or insights it provides. Generalization involves deliberately excluding some data to make them less identifiable.
Data can be modified within a series of ranges with logical limits. The result is a reduced granularity of the data, making it difficult or even impossible to retrieve the exact values associated with an individual.
The goal is to remove certain identifiers without compromising data accuracy. For example, you can remove or replace the house number of a specific address, but the street name will not be removed.
In certain cases, it is possible to generalize the information by classifying it into groups, as would be the case when replacing the exact ages of individuals in a database by age groups (65-74, 75-84, 85 , etc.).
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In addition to enabling organizations to comply with regulatory laws, including GDPR, anonymization techniques promote digital transformation in businesses, providing anonymized and protected data that will be used to generate new market value.
No organization can do anything without a secure and consistent database. These techniques isolate data governance and help maintain privacy from intruders, while acting as a barrier against outside influence.
Since 2020, Pangeanic has been leading the "Multilingual Anonymization toolkit for Public Administrations" project, which is supported by the European Union's CEF (Connecting Europe Facility) program and the NTEU (Neural Translation for the EU) project.
The goal of MAPA is to develop a multilingual data anonymization tool, based on named entity recognition (NER) and applicable to all EU languages. With this tool, European public administrations will be able to share data in compliance with the requirements of the GDPR, while protecting the privacy of their users. This project is being carried out with shared open-source code in order to facilitate the development of this data anonymization technology.
Absolute anonymization is very difficult to achieve, since guaranteed and irreversible anonymization of a dataset is practically impossible in most cases.
Taking this into account, it is necessary that, at the very least, the re-identification that could occur would entail such a large effort that it would not be feasible for the person trying to recover the data.
On the other hand, non-reversible and more stringent forms of data anonymization may restrict the ability to extract meaningful information from the results, so their use in some cases loses value compared to the original version.
It is, therefore, important to study each case and find the right balance between hermetically protecting the user's security and privacy, and maintaining some of the characteristics of the data in a way that will continue to be useful.