W3C publishes draft guidance to help non-experts assess differential privacy systems
The W3C Privacy Working Group has released a draft note explaining how differential privacy systems work and outlining key factors that non-specialists should consider when reviewing such technologies.
The Privacy Working Group of the World Wide Web Consortium (W3C), an international standards body that develops technical guidelines for the web, has published a first draft of a document titled Considerations for Reviewing Differential Privacy Systems (for Non-Differential Privacy Experts).
The draft, released on 12 February 2026, is intended to help people who are not specialists in advanced privacy engineering understand and assess systems that use a technique known as differential privacy.
Differential privacy is a mathematical approach designed to protect individuals’ data when organisations analyse large datasets. Instead of removing personal data entirely, the technique introduces carefully calibrated “noise” or random variation into statistical results. This makes it difficult to determine whether any specific individual’s data was included in the dataset, while still allowing organisations to extract useful aggregate insights.
For example, a public authority might want to publish statistics about how many people in a city use a particular service. With differential privacy, the published figures would include small adjustments that protect individual identities but still reflect overall trends. The goal is to reduce the risk that someone could reverse-engineer published results to identify specific individuals.
The newly published draft does not propose new technical standards. Instead, it serves as explanatory guidance. It outlines the main trade-offs involved in designing and deploying differential privacy systems. These trade-offs typically involve balancing privacy protection against data accuracy, deciding how much statistical noise to introduce, and determining how many parties must be trusted to manage or process the data securely.
The document is aimed at policymakers, auditors, civil society organisations, and other stakeholders who may need to evaluate whether a system claiming to use differential privacy does so appropriately. It identifies key questions to consider, such as how privacy parameters are set, how data is processed, and what assumptions are made about potential attackers.
By providing structured guidance, the draft seeks to make complex privacy engineering concepts more accessible to non-specialists. As digital services increasingly rely on large-scale data analysis, differential privacy has become more prominent in areas such as online advertising measurement, public statistics, and platform analytics.
The draft is open for review and feedback as part of the W3C’s public consultation process.
