Differential privacy is a mathematical framework for enabling statistical analysis and machine learning on sensitive datasets while protecting the privacy of individuals within those datasets. In AI assurance, differential privacy is a key technique for demonstrating compliance with data protection laws and building user trust in AI systems that rely on personal or confidential data.

The core idea of differential privacy is to introduce carefully calibrated random noise to queries or model parameters so that the presence or absence of any single individual in the dataset cannot be reliably inferred. This provides provable privacy guarantees while still allowing aggregate insights to be drawn.

In practice, differential privacy can be implemented at various stages of the AI pipeline:

• During data aggregation or analysis (query-based privacy).

• While training models on sensitive datasets (training-level privacy).

• In model deployment via privacy-preserving APIs (output-level privacy).

Differential privacy is particularly important for applications involving health data, location tracking, financial records, or biometric identifiers — contexts where breaches can cause harm or violate legal protections. Its adoption is often required or recommended under laws such as GDPR, HIPAA, or the UK Data Protection Act.

Assurance of differential privacy includes:

• Verifying the privacy budget (ε) and noise mechanisms used.

• Ensuring that privacy settings are appropriately tuned to the use case.

• Testing for leakage or vulnerabilities to re-identification attacks.

• Reviewing whether data minimisation principles are upheld.

It is also relevant in the creation of synthetic datasets used for testing or validation. When real-world data is augmented or simulated, differential privacy ensures that synthetic records do not inadvertently reveal characteristics of real individuals.

Challenges in assurance include balancing privacy with model utility, verifying correct implementation, and aligning mathematical guarantees with regulatory expectations. As part of broader AI assurance efforts, differential privacy is assessed alongside governance controls, consent mechanisms, and data handling protocols.

Differential privacy is increasingly seen not only as a compliance tool but also as an enabler of responsible innovation. It allows AI developers to work with valuable data while preserving individual privacy, reducing risk, and fostering public confidence.

In sum, differential privacy supports AI assurance by offering formal, testable guarantees of privacy protection — making it a cornerstone of secure, ethical, and lawful AI development.