More and more research—whether medical, social, or academic—involves handling sensitive personal data. This opens up opportunities for scientific advancement but also raises a fundamental challenge: how can we ensure data protection in scientific research without compromising data quality?
One of the most critical aspects is the method chosen to protect that data. And not all methods work the same. A study published in PLOS Digital Health shows how certain traditional approaches can severely impact data utility.
In this article, we analyze the pros and cons of encryption and tokenization to ensure data protection in scientific research. You will discover how international regulations demand effective mechanisms, which method best preserves data utility, and how technologies like Nymiz allow research to progress without compromising privacy.
Impact of anonymization on data accuracy
The study revealed that the statistical accuracy of scientific data drops by up to 78% after applying certain traditional anonymization techniques. The direct consequence: less reliable models, loss of analytical value, and, in many cases, the inability to reproduce results. This challenges the credibility of data protection in scientific research.
This represents not only a technical issue but also an ethical one. Science based on poor or distorted data loses credibility and impact. A robust data protection in scientific research strategy must maintain both privacy and scientific rigor.
Limitations of encryption in research environments
Encryption transforms data into an unreadable format using cryptographic keys. It prevents unauthorized access and is useful for protecting confidentiality during storage or transmission.
In research, encryption prevents analysis while data is encrypted. Statistical models, AI systems, or real-time dashboards cannot operate on encrypted data.
Moreover, secure key management is critical, and any error can render data inaccessible or vulnerable. These limitations affect the effectiveness of data protection in scientific research where collaboration and analysis are key.
Requirements of International Regulations
Ensuring data protection in scientific research isn’t just a best practice—it’s a legal obligation. Major international frameworks have established clear requirements:
GDPR (EU): Demands data minimization, pseudonymization where possible, and accountability to demonstrate compliance (Articles 5, 25, 32).
AI Act (EU): Establishes obligations for high-risk AI systems that use personal data, requiring strict risk assessment and robust privacy safeguards.
HIPAA (US): Requires covered entities and business associates to protect health information using physical, technical, and administrative safeguards.
These regulations converge on the principle that privacy must be demonstrable, auditable, and technically enforced.
Requirements for processing personal data
International laws don’t just suggest protective measures—they demand implementation of appropriate technical and organizational safeguards. This includes:
Effective anonymization or pseudonymization tailored to the type of data and risk level.
Risk assessments that justify the chosen data protection strategy.
Documented policies for data lifecycle management, reidentification risk, and third-party access.
For example, tokenization with AI meets the GDPR’s requirement for “data protection by design and by default,” enabling analysis without exposing identifiable information.
Penalties for non-compliance
Failing to apply adequate safeguards has severe consequences:
Under GDPR: fines up to €20 million or 4% of global turnover.
Under HIPAA: up to $1.5 million per violation category per year.
Under AI Act: strict limitations or bans on high-risk system deployment.
By implementing intelligent tokenization, institutions can reduce exposure to these risks while enabling compliant data protection in scientific research.
Types of sensitive data in scientific research
Common examples of personal data:
- Medical records or clinical data
- Interview transcripts
- Sociodemographic variables (age, gender, education level, location)
- Health conditions or risk factors
- Psychological or neurological records
When this data is processed or shared, choosing the right protection method is essential.
Use Case: Tokenization in clinical trials to link data with Real-World Data (RWD)
In clinical research, it is common for data collected during clinical trials to be isolated from real-world data (RWD), such as electronic health records or insurance claims. This disconnection can limit the full understanding of the patient journey and long-term treatment evaluations.
Implemented solution
To address this challenge, clinical research organizations have implemented tokenization solutions that allow secure linkage of clinical trial data with multiple RWD sources.
Using this anonymization model, real information is replaced with pseudonyms that maintain the data’s nature but eliminate identifiable details. This enables high privacy standards and regulatory compliance.
Observed benefits
- Evidence improvement: data linkage allows for a more comprehensive assessment of treatment effectiveness and safety under real-world conditions.
- Regulatory compliance: the tokenization process ensures patient privacy in line with regulations such as the GDPR and HIPAA.
- Operational efficiency: data integration reduces duplication of effort and improves efficiency in data collection and analysis.
This approach shows how tokenization can be a powerful tool to enhance data protection in scientific research, providing a more holistic view of the patient and improving the quality of generated evidence.
Advantages of AI-based tokenization by Nymiz
What is tokenization?
Tokenization replaces sensitive values with controlled pseudonyms. What sets Nymiz apart is that this process adapts to the data type, the study goal, and the risk context.
How it enhances data analysis
Our AI preserves the structure, semantics, and context of the data, allowing for statistical analyses, AI modeling, and visual explorations without needing to reverse the process.
Verified benefits
In validation tests, we maintain up to 95% analytical accuracy, ensuring that study quality is not compromised by the anonymization process. Additionally, we automate the workflow, saving up to 80% of the time compared to manual methods.
When to use encryption and when tokenization
Ideal scenarios for encryption
Encryption is useful for protecting confidentiality during storage or transmission, especially in closed environments.
Ideal scenarios for AI-based tokenization
But if your goal is to publish, analyze, or reuse data ethically and securely, AI-based tokenization is the best option. It allows you to protect without blocking, and progress without sacrificing privacy.
Nymiz: Your ally in data protection without compromising usability
The debate should not be between privacy or utility. Today, it is possible to have both. The key is to choose the right tool for each context.
With Nymiz, you can protect sensitive data and preserve the analytical power your research needs. In this way, individual rights and scientific progress go hand in hand.
Want to learn how to apply this to your own data? Book a demo and discover it firsthand.
