Determining how to secure data and preserve privacy can be daunting for data scientists. Working with sensitive data raises questions about what data to use and how to protect it. It’s easy to feel overwhelmed and opt for using raw data, which exposes your analysis and models to information leakage and vulnerabilities.

Our goal is to make this process easier for you. So, in this post, we’ll guide you on navigating the complexities of data privacy, ensuring you can focus on data science while protecting data.

Recognise the importance of data privacy

Determining the value of data and the need for privacy is the first step in your journey.

While it may seem obvious, taking a moment to reflect on the necessity of the data you’re collecting is crucial. Ask yourself if you truly need the data for your project and consider the potential risks and benefits of using it.

Explore alternatives

Sometimes, you collect more data than you actually need.

Consider whether there are non-personal data sources or variables that can serve as substitutes. By removing unnecessary data, you can reduce privacy risks while still obtaining valuable insights.

Apply privacy-preserving techniques

If personal data is necessary, it’s vital to implement privacy-preserving techniques.

For instance, explore k-anonymity, l-diversity, and t-closeness to protect against identification attacks. These techniques group individuals together, making identifying specific individuals within the dataset challenging.

Some other techniques include:

  1. Differential privacy: Differential privacy is a mathematical framework that aims to protect individual privacy while extracting useful insights from datasets. It adds a controlled amount of noise to query results to provide plausible deniability and prevent the identification of specific individuals.
  2. Homomorphic encryption: Homomorphic encryption enables computations to be performed on encrypted data without decrypting it, ensuring that sensitive data remains encrypted throughout the analysis. It allows for privacy-preserving computations on encrypted data.
  3. Secure Multi-Party Computation (SMPC): SMPC is a cryptographic protocol that enables multiple parties to jointly compute a function while keeping their inputs private. It ensures that no single party learns any sensitive information about the others’ inputs.

Establish a baseline analysis

To ensure privacy while obtaining meaningful results, start by conducting an initial analysis using minimal personal data or privacy-preserving techniques. This baseline analysis will help you evaluate the sufficiency of the available data and identify any privacy concerns.

Know about privacy-preserving machine learning

When planning to release a machine learning model trained on personal data, it’s essential to be aware of privacy-preserving machine learning methods. You can do so by staying updated on research in this field. This way, you can implement techniques that preserve individual privacy while achieving accurate and effective models.

Consider aggregated anonymisation

If your goal is to share data analysis results, consider aggregated anonymisation techniques.

These techniques allow you to provide valuable insights while maintaining individual privacy. Aggregating data removes identifying information while still offering meaningful and valuable findings.

Safely share models or analyses internally or externally

If you need to share internally used models or analyses externally, employing privacy-preserving methods similar to those used during model development is crucial.

For example, ensure that personal data is not included in the shared materials, and carefully evaluate the potential risks associated with sharing any data, even if it has been anonymised.

Secure APIs and prevent unauthorised access

If you have released a model with an open API, take steps to secure it and prevent unauthorised access. Further, collaborate with your security and engineering teams to implement measures that protect against malicious access and potential security breaches.

Actions to take next

  1. Worry less about data privacy compliance for your data or AI systems by entrusting us to conduct thorough assessments with your data scientists.
  2. Equip data scientists with the knowledge and skills to conduct privacy impact Assessments by joining our Data Protection Programme. Our modules on Conducting privacy impact assessments and Managing the data protection risks of AI projects will guide them through the process.
  3. Gain insights into the impact of data protection on data science by completing our quick and free organisational impact assessment tool. This assessment will help data scientists assess the potential privacy risks and implications.
  4. Ensure data scientists conduct PIAs correctly by seeking our expert review of their process and outcomes. Our team will provide valuable feedback and validation to enhance compliance efforts.