As cities become increasingly digitised, the demand for vast amounts of data to drive innovation, improve public services, and enhance urban planning has never been greater. However, the collection and use of real-world data raise significant privacy concerns, as well as ethical and logistical challenges. This is where synthetic data emerges as a critical solution. Synthetic data refers to artificially generated data that mimics the characteristics of real data without compromising personal privacy or sensitive information. It provides a pathway to maintaining data utility while sidestepping the risks associated with handling real-world datasets.
Synthetic data is generated using advanced machine learning models that analyse patterns, structures, and relationships within existing datasets. These models then produce new data points that statistically resemble the original data but do not correspond to real individuals or events. The technology ensures that the synthetic data retains the essential qualities needed for analysis and decision-making, while simultaneously eliminating the potential for privacy breaches. This is particularly valuable in urban contexts, where data derived from sensors, cameras, and other sources can be rich in detail and highly sensitive.
As urban areas continue to evolve into smart cities, relying on extensive data for functions ranging from traffic management to public safety, synthetic data offers a secure and scalable way to train AI systems, simulate urban scenarios, and conduct research without exposing real individuals to privacy risks. Furthermore, synthetic data allows for the testing of new technologies and policies in a risk-free environment, fostering innovation without unintended consequences.
In essence, synthetic data not only solves the pressing issue of data privacy but also propels the development of urban technologies forward. By enabling safer, more ethical data practices, synthetic data supports the creation of smarter, more resilient cities that can adapt to the needs of their inhabitants while safeguarding their privacy.
A data platform that models the built environment and human movement patterns to help public agencies make informed decisions.
Synthetic data generation platform for autonomous systems.
Pioneers in AI-generated synthetic data for enterprise and insurance.
Developing foundation models for robotics (Project GR00T) and vision-language models like VILA.
Privacy engineering platform offering synthetic data generation APIs.
Software company for traffic planning and transport logistics.
Paper
A Comprehensive Evaluation Framework for Synthetic Trip Data Generation in Public TransportarXiv · Sep 16, 2025
Proposes a Representativeness-Privacy-Utility (RPU) framework to evaluate synthetic trip data for public transport. The study benchmarks twelve generation methods to address privacy and accessibility challenges in smart card data usage.
Article
Synthetic Mobility Data: The Future of Smart Urban PlanningSocietyByte · May 2, 2025
Discusses how synthetic mobility data offers a solution for smart cities to harness movement patterns for urban planning and traffic optimization while strictly protecting individual privacy.
Paper
Practical Applications of Synthetic Data GenerationStatistics Canada · Jul 29, 2025
Discusses the use of synthetic data generation for privacy-preserving data sharing and de-biasing in official statistics. It presents case studies and evidence of performance across various use cases.
Article
Synthetic Data Is About To Transform Artificial Intelligenceforbes.com
Imagine if it were possible to produce infinite amounts of the world’s most valuable resource, cheaply and quickly. What dramatic economic transformations and opportunities would result?
Article
Why Synthetic Data Is Key To Paving the Way for Smart Citiesspiceworks.com
With the increasing need for operational efficiency and process automation across the board, from small-scale projects to the magnitude of smart cities, the demands for data continue to become more exacting. Steve Harris, CEO of Mindtech, explains how achieving them with real-world data is challenging, thus launching synthetic data into the spotlight to address these shortfalls. The potential for uniting synthetic and real-world data in the pursuit of smart cities is highly promising for the future of smarter AI.
Article
Governing Smart Cities: Use Cases for Urban Transformation | World Economy Forumwww3.weforum.org
Many smart cities still lack the basic governance and policies needed to ensure the adoption of technology in a responsible and ethical way. This matters because cities are accelerating the use of digital tools to access real-time intelligence and target interventions to save lives. But such technology brings privacy, equity, accessibility and cybersecurity risks. The G20 Global Smart Cities Alliance has helped cities leapfrog these challenges by publishing seven model policies in high-priority areas. Since developing model policies is just the first step, the alliance has also developed regional networks in Japan, South-East Asia, India and Latin America to support city leaders in implementing and tailoring these policies to suit the local context. This report profiles three pilot programmes supported by the alliance that tackle three different policy challenges in Mexico City, Tsukuba and Istanbul.
Article
Top 10 Emerging Technologies of 2024 | World Economic Forumweforum.org
The Top 10 Emerging Technologies report is a vital source of strategic intelligence. First published in 2011, it draws on insights from scientists, researchers and futurists to identify 10 technologies poised to significantly influence societies and economies. These emerging technologiesare disruptive, attractive to investors and researchers, and expected to achieve considerable scale within five years. This edition expands its analysis by involving over 300 experts from the Forum’s Global Future Councils and a global network of comprising over 2,000 chief editors worldwide from top institutions through Frontiers, a leading publisher of academic research.
Article
Synthetic data use: exploring use cases to optimise data utilitylink.springer.com
Synthetic data is a rapidly evolving field with growing interest from multiple industry stakeholders and European bodies. In particular, the pharmaceutical industry is starting to realise the value of synthetic data which is being utilised more prevalently as a method to optimise data utility and sharing, ultimately as an innovative response to the growing demand for improved privacy. Synthetic data is data generated by simulation, based upon and mirroring properties of an original dataset. Here, with supporting viewpoints from across the pharmaceutical industry, we set out to explore use cases for synthetic data across seven key but relatable areas for optimising data utility for improved data privacy and protection. We also discuss the various methods which can be used to produce a synthetic dataset and availability of metrics to ensure robust quality of generated synthetic datasets. Lastly, we discuss the potential merits, challenges and future direction of synthetic data within the pharmaceutical industry and the considerations for this privacy enhancing technology.
Article
Why Synthetic Data helps with Big Data Privacy (Part 4/5)youtube.com
Synthetic Data allows organizations to innovate with their valuable big data assets without putting their customers' privacy at risk. In the fourth part of our mini video series on Synthetic Data we will cover how exactly Synthetic Data enables privacy-friendly innovation.
Article
What Is Synthetic Data?blogs.nvidia.com
Synthetic data generated from computer simulations or algorithms provides an inexpensive alternative to real-world data that’s increasingly used to create accurate AI models.
Article
Is Synthetic Data the Future of AI?gartner.com
Synthetic data is often treated as a lower-quality substitute and used when real data is inconvenient to get, expensive or constrained by regulation. However, this reaction misses the true potential of synthetic data. Gartner estimates that by 2030, synthetic data will completely overshadow real data in AI models.
Article
Report investigates how synthetic data can be used in governmentadruk.org
In a report published today, the Behavioural Insights Team (BIT) summarised their investigation into the uses of synthetic data in government. They also discussed the different types, benefits, and challenges of creating and using synthetic data, making recommendations for ADR UK, government and stakeholders outside of government.
Article
On synthetic data: a brief introduction for data protection law dummieseuropeanlawblog.eu
Synthetic data is attracting increasing attention from technicians and legal scholars in recent years. This is especially noticeable among entities and people working on data-driven technologies, particularly in the artificial intelligence application development and testing sector, where sheer volumes of data are needed. In these circles, synthetic data has become a growing trend under the “fake it till you make it” concept by promising to alleviate existing data access and analytics challenges while respecting data protection rules. Given the rising prospects and acceptance of data synthesis, there is a need to assess the legal implications of its generation and use, the starting point being the legal qualification of synthetic data.
