You have surely heard about how Big data has turbo-charged the AI revolution, and now why are we focusing our attention on data minimization, which seems to be the diametrically opposite of big data. Is it even possible for AI? Let's explore!

Data minimization definition

Data minimization refers to the collection and use of the minimum amount of data for a specific purpose. It has several advantages and applications, most crucially as a privacy enhancing mechanism as well as an efficient data management and processing method.

When it comes to data privacy, data minimization refers to limiting or reducing personal data, that is personal information about individuals like their name, contact, and online activity etc.

Data minimization and GDPR

Data minimization is a very well known practice in the privacy preservation and compliance circles, since regulations like GDPR, and the CPRA, the amended version of the California Consumer Protection Act even mandate it. Data minimization is one of the foundational principles of GDPR requiring those who collect and process personal data to stick to only data that is "adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed."

Data minimization vs purpose limitation

Since data minimization is related to a specific purpose, it may be mixed up with another crucial privacy principle known as purpose limitation. While data minimization’s focus on the collection and usage of data, the purpose limitation means when you collect data for a specific purpose, you should not use it for a different one.
According to GDPR, purpose limitation requires that ‘personal data should only be collected for specified, explicit, and legitimate purposes and not further processed in a manner that is incompatible with those purposes.’

Data minimization examples

Legalese much? Here's a practical example for data minimization: If you run an online clothing store, minimally, you need to collect the name, contact details, and payment information of customers to offer your services, but don't need to know their marital status to do so.

Similarly, an example for purpose limitation is, a doctor from a private hospital collects patient data for treatment purposes. The holding that owns the hospital also has wellness resorts and would like to access the patient data to promote their special health and wellness packages. According to the purpose limitation principle, the reuse of data for promotion is not compatible with the original purpose of treatment, unless the holding has explicit consent of the patients to do so.

Coming back to data minimization, it’s an essential privacy by design mechanism to protect individual rights, and reduce the privacy ramifications in case of data breaches. IBM backed security think tank Ponemon Institute estimates that the average cost of a data breach in 2020 was $3.86 million, which includes business disruptions, legal fees, and regulatory fines. The higher the personal data you process, the higher this regulatory risk.

Data minimization techniques

Data minimization is not all about privacy. Data collection is not only expensive but storing and managing the collected data is resource intensive. From an efficiency perspective, data minimization reduces the amount of storage and processing infrastructure and resources, increases processing speeds, bringing about performance, economic and environmental benefits. So whether for privacy or efficiency, the crux of data minimization is- collect and use the minimal necessary data and not more, and not less!

Sounds cool, but it's easier said than done, especially when it comes to AI models. We know that the most powerful AI models require large amounts of data to provide robust results. So how do we stick to the data minimization principle and still build and train great AI models?

Synthetic data for data minimization

A neat path to data minimization is through the use of synthetic data. Synthetic data is artificially generated data that mimics the statistical properties of real data but does not contain any personal information. By using synthetic data you can train AI models and respect the data minimization principle for personal data, since you are not using real data but real-looking data. Synthetic data significantly reduces the risks of personal data breaches, saving millions of dollars in regulatory fines, as well as protecting individuals' right to privacy. For context, personal data breach fines under GDPR may cost companies €20 million euros or 4% of their annual turnover, whichever is higher.

Specific to the data efficiency side of data minimization, the major advantage of synthetic data is its on-demand generation possibility, bypassing the need to collect and store large amounts of data- personal or otherwise. Synthetic data also offers greater quality control, as companies can specify the data characteristics, such as demographics, behavior, and preferences, to improve the accuracy of machine learning models. Synthetic data can also be used to simulate different scenarios, such as rare events or extreme conditions, which may be difficult to replicate using real data.

Why do we use minimization techniques?

To summarize, data minimization fosters data privacy and offers efficiency benefits by reducing collection, computational, storage and processing efforts. Synthetic data is a fantastic tool for data minimization. As organizations continue to collect and process large amounts of data, the use of synthetic data will become increasingly important in achieving data minimization and protecting individuals' privacy as well as improve data processing efficiency.