Trusting the data behind the Smart City
Smart cities increasingly rely on AI-driven insights from video data, yet trust remains the weakest link. Can cities, solution providers and citizens rely on data streams being authentic, anonymised and fit for purpose, especially when decisions become safety- and trust-critical?
In a real-world demonstration case at WE BUILD DENMARK’s living lab, DOLL Living Lab, Milestone Systems, Felicity Smart Infrastructure and Scurid are exploring exactly that question. In this joint partnership, they are testing how video data from urban environments can be validated, anonymized and documented without disrupting everyday operations.
The case builds on Milestone Systems’ newly launched Project Hafnia technology. It addresses one of the biggest challenges in AI development today: access to high-quality, compliant and trustworthy data — supported by Scurid’s integrity and provenance technology, which is camera-manufacturer agnostic and runs on the edge to cryptographically verify what was captured, when it was captured, and which camera produced it—without relying on cloud connectivity.
What is the challenge?
Smart City services increasingly depend on absolute confidence in the data behind the algorithms. From traffic prioritization to safety and mobility management, decisions are becoming safety- and trust-critical.
If a system gives priority to a vehicle at a junction, or informs planning decisions that affect citizens’ daily lives, the data must be provably authentic.
At the same time, cities must ensure that data streams are privacy-preserving, anonymized and compliant with governance requirements. The fundamental challenge is therefore not just what data can do – but whether it can be trusted.
Which kind of results do the companies expect?
In the demonstration, selected cameras at DOLL capture real traffic and pedestrian flows. Each video frame is cryptographically ‘stamped’ at the source, ensuring that time, place and origin can later be verified. Combined with deep anonymization techniques, this makes it possible to create data streams that are both privacy-preserving and auditable.
For AI and solution providers, the case enables valuable access to validated, ethically sourced video data that can be used to train and improve AI models, with clear data lineage and compliance built in from the start.
Rather than relying on synthetic data or fragmented datasets of uncertain origin, developers can work with real-world data streams and trust their quality.
What value does it aim to achieve?
The demonstration is not about a finished product, but about exploring how trust can be designed into future digital infrastructure, before regulations, scale and complexity make it harder.
At the same time, cities and citizens gain transparency and reassurance that privacy is respected and governance requirements are met.
As AI moves further into public space, initiatives like this help answer a fundamental question for Smart Cities: not just what data can do – but whether we can trust it.
The future of AI in public space depends on getting two things right at the same time: performance and trust. You can’t have one without the other.
Ben Cahill
Senior Engagement Manager, Data & Insights Platform, Milestone Systems
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