What is data minimization? Key tools and techniques

That’s the idea behind data minimization: a principle that helps organizations reduce unnecessary data collection by only collecting and handling the data that’s truly necessary.

Data minimization is a principle of collecting, storing and using only the personal data needed for a specific purpose.

The key is to act with intention. If the information isn’t directly useful or relevant to the task at hand, it shouldn’t be collected. And once it’s no longer needed, it should be securely deleted.

This concept is a cornerstone of modern privacy laws around the world, such as the European General Data Protection Regulation (GDPR).

At its core, data minimization is about reducing the risks that come with managing personal information, from data breaches and cyberattacks to regulatory penalties for non-compliance with privacy laws. By limiting data to only what is necessary, organizations can better protect sensitive information, reduce legal risk, and avoid becoming an easy target for threats. 

But it goes beyond security and compliance. Data minimization also helps build trust. If companies show that they only request and use data that’s truly necessary, they demonstrate respect for user privacy and send a clear signal to customers and partners that their data is in good hands.

Organizations can apply data minimization in many practical ways, ranging from how they process it, to how they protect personal data behind the scenes.

These are well-established methods used to reduce the identifiability or exposure of personal data in day-to-day operations.

• Data masking: Transforming sensitive data into a non-identifiable format, often by replacing real values with fictitious, scrambled ones that retain the same structure. It ensures that the original data remains protected while still allowing systems or teams to use the masked version for testing, training, or analytics.

• Anonymization: Altering personal data in a way that makes it impossible to link the data back to an individual, even indirectly. This protects individuals’ data from unauthorized exposure by rendering it or making it permanently unidentifiable.

• Tokenization: Replacing sensitive data with a non-sensitive placeholder, or token, that holds no exploitable value on its own. The original data is securely stored in a separate system, and the token acts only as a reference to it. This means even if the token is exposed, it reveals nothing useful to attackers.

Privacy-Enhancing Technologies (PETs) play a critical role in putting data minimization into practice. They don’t always reduce the amount of data collected, but they reduce how much is exposed, accessed, or shared, which enables organizations to get value from sensitive information without compromising privacy.

By embedding PETs into workflows and digital systems, businesses can process only what’s necessary, when it’s necessary, and that aligns directly with the data minimization principle.

• Multi-Party Computation (MPC): Enables two or more parties to jointly compute a function on their combined data without sharing the raw inputs. Ideal for secure data collaboration between organizations.

• Zero-Knowledge Proofs (ZKPs): Allow someone to prove a fact (like “age-over-eighteen”) without revealing any underlying data (i.e., for example the actual age). Useful in digital identity and access control.

• Federated Learning: Allows machine learning models to be trained across decentralized devices or datasets without transferring sensitive data to a central server.

• Homomorphic Encryption: Enables computations on encrypted data, so the data never needs to be decrypted during processing, which minimizes exposure.

• Differential Privacy: Adds statistical “noise” to datasets, making it impossible to identify individuals while still allowing aggregate insights with a very small margin of errors.

While many data minimization techniques focus on handling personal data after it’s been collected, at Partisia we take a proactive approach.

Our goal is to prevent unnecessary data from being shared in the first place without undermining the utility of the data. This reduces the risk of data breaches, misuse, or non-compliance before they ever occur.

To achieve this, we use Privacy-Enhancing Technologies (PETs) that ensure only the minimum amount of personal data is processed, shared, or exposed. These tools help organizations comply with the data minimization principle by limiting access to sensitive datasets while still enabling secure data sharing and analysis.

Here’s how we apply them in practice:

• Multi-Party Computation (MPC): Allow multiple parties to jointly compute a result without ever exposing their individual data inputs. The only thing revealed is the final outcome, not the input data behind it. For example, pharmaceutical companies can collaborate on clinical research to develop new medicine — without sharing sensitive patient data.

• Selective Disclosure and Zero-Knowledge Proofs (ZKPs): Used in our Decentralized Identities solution, these methods enable individuals to share only the specific data, or prove a fact, required in a given context and avoid oversharing personal data. For example, a user can prove they’re over 18 to access age-restricted services without revealing their full ID.

By designing with privacy in mind from the start, we help businesses embed data minimization into the very foundation of their digital services.

Want more insights on data protection, privacy-enhancing technologies, and how to build trust through smarter data practices in industries like finance, healthcare and education?

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