How differential privacy complements anonymization to ensure data security

Undoubtedly, the intelligent use of data is a vital and strategic action for any company or research organization. However, this legitimate exploitation of data is limited by the need to preserve the right to privacy of data subjects.

Thus, tools and techniques for the anonymization of sensitive data have emerged. However, these are techniques that, on their own, may lead to the loss of the value of the information or pose certain risks of being reversed.

For this reason, it is necessary that anonymization includes statistical methods and uses machine learning algorithms that allow you to achieve differential privacy in your data. In this article, we will delve into the definition of differential privacy, how it works and its applications.

What is differential privacy and how does it work?

Differential privacy (DP) is a series of methods and techniques that facilitate the collection and analysis of data without compromising the right to privacy of data subjects, eliminating the possibility of knowing whether or not a particular individual’s data is included in the analysis.
Under a mathematical analysis, the definition of DP encompasses several statistical tools, allowing the controlled introduction of random data (noise) to the set under study.

In this way, the direct or indirect connection that may exist between the information and the individual who provided it is hidden, but the appropriate accuracy for the data to remain useful is maintained.
Of course, the more noise you add, the less useful the data will be, although more privacy is obtained. However, as noted, the noise that is added is controlled, so the privacy loss parameter ε is used to indicate how much noise can be introduced to obtain accurate data.

What is the value of ε? To know it, it is necessary to find the ratio of optimal privacy/accuracy and determine ε from the Laplace distribution (statistical probability).

While the value of ε tends to be smaller, the results of the data analysis will be less accurate, but more protected. On the other hand, while the value of ε is higher, the accuracy of the results will increase, but the privacy of the data will be greatly compromised.

How is a good relationship between accuracy and data privacy achieved? This is where the use of machine learning algorithms comes in, which, through continuous improvements, yield the most accurate results. Two models can be used: algorithmic models that guarantee local differential privacy and those that allow global differential privacy.

 

The need for differential privacy

Differential privacy is necessary to enable the publication of data in the day-to-day management of companies or research institutes.

There are other tools widely used in data protection, such as those that remove identifying values (names, IP addresses, etc.). These mechanisms, however, have certain limitations. There is even sufficient evidence to prove that, by submitting processed data, they can be linked to the use of other databases and lose privacy.

Consequently, it becomes necessary to achieve differential privacy through the inclusion of controlled randomness within the machine learning algorithm, allowing the continuous training of the system and making it difficult to detect the behavior of the analysis model.
This is one of the keys to the success of data anonymization systems that use artificial intelligence and machine learning. Thanks to the use of advanced algorithms, anonymization can provide accurate data and make reverse engineering impossible.

 

 

Characteristics and benefits of differential privacy

Starting from the same definition of differential privacy, 3 key features are extracted from this mathematical idea:

  • Measuring data privacy loss. This therefore facilitates the control and balance of data privacy and accuracy.
  • Composition. DP is characterized by its differential and parallel composition. The first helps to run multiple analyses, separately, within a single data set. The second, in turn, allows a data set to be divided into several unconnected fragments in order to execute, in each fragment, the techniques included in DP.
  • Post-processing. It is completely safe to perform any calculation or further processing on differentially private data. This is because there is no chance of reversing the process.

The main benefits of differential privacy include:

  • It offers a guarantee, under mathematical verification, of withstanding various types of data privacy attacks, such as linkage attacks, differential attacks and reconstruction attacks. In this way, differential privacy and the GDPR are compatible.
  • It has a compositional analysis structure, making it easy to estimate the total privacy loss when running two analyses on the same data set; only the individual privacy losses from each analysis need to be added up.

 

Applications of differential privacy in businesses

As is well known, the foundation and success of most businesses is the intelligent use of data. This use involves capturing, analyzing, and detecting trends, patterns, and connections between data, always in order to extract the maximum value and benefit from them to solve problems within the company.

For this reason, the applications of differential privacy in businesses are diverse, because it helps them to freely exploit data and execute key operations within business management, such as collecting user behavior or publishing and sharing data with other organizations.

Thanks to the various applications of differential privacy, companies can access a large volume of sensitive and confidential data for business and research purposes and without any risk of violating the customers’ right to data privacy, always complying with the GDPR.

 At Pangeanic we can help you safeguard data privacy while working with accurate and useful information for assertive decision making. We are leaders in the development of PLN technology and the use of AI, which is why we make our anonymization software available to you: Masker.

 

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