UtiP-DAM

Understanding how people move around is crucial for everyone—governments planning for climate change, companies building better city experiences, and healthcare officials tracking disease outbreaks. But there’s a catch: tracking individuals raises serious privacy concerns.
The problem? Simply blurring names isn’t enough to protect an individual’s privacy, as their movement patterns alone can reveal their identity. The classic centralized approach, where individuals must trust a single entity with their data, no longer works. A new system is needed—one where privacy and empowerment go hand in hand.
That’s where the UtiP-DAM project comes in: it implements a decentralized method for anonymizing mobility data, ensuring anonymization happens where the data is collected, rather than by a central entity. But there’s more! UtiP-DAM provides anonymization verification tools that allow individuals to check if their mobility data has been distributed in public datasets, and it offers tools for organizations to audit their datasets for de-anonymization risks and re-anonymize them using the UtiP-DAM algorithm.
In short, UtiP-DAM empowers individuals and organizations to contribute to mobility research without sacrificing privacy. It’s like having your cake and eating it too—gaining valuable insights for better cities while preserving the freedom to move without a trace.
Repositories:
Website: https://ngi.cs.co.il/
GitHub: https://github.com/NGI-TRUSTCHAIN/UtiP-DAM
Currently open to the TrustChain community only. Reach out if you need access.
- Motivation for the project: Mobility data collection often lacks consent. UtiP-DAM empowers data controllers to anonymize mobility data at the point of collection, enhancing privacy. With UtiP-DAM, TrustChain becomes a more robust ecosystem where mobility data can be shared without compromising on individuals’ right to privacy.
- Generic use case description: IIoT sensors capture location data in a city and anonymize data locally with UtiP-DAM. No individual paths are released, but valuable insights are still gained (e.g: congested routes.). City goers can check if their own data is included in public datasets.
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Essential functionalities: Above are draft mockups of the UtiP-DAM web pages where users will be able to use the functionalities developed during the project
→ anonymity verification for individuals: input location point and schedule to verify if one’s journey is included in a publicly available dataset
→ anonymity verification for companies: input their dataset, and verify if they are properly anonymized
→ anonymizer for companies: Input their non-anonymized datasets and receive as output an anonymized database using centralised k-anonymity. - How these functionalities can be integrated within the software ecosystem: The functionalities will be available via the NGI Trustchain > UtiP-DAM GitHub repository for free download and via the dedicated webpage hosted by Correlation Systems. This enables both a free and open-source use of the functionalities and a pay-per-use model for Correlation Systems.
- Gap being addressed: Current data anonymization risks user privacy due to central control of the data controller. UtiP-DAM decentralises anonymization, instead processing data at its point of collection. Open-source tools will let institutions anonymize data, verify privacy, and individuals check if their data is shared publicly. All to bridge the gap between privacy concerns and practical solutions.
- Expected benefits achieved with the novel technology building blocks: UtiP-DAM enables privacy rights to be better protected by processing mobility data at their point of collection, sharing only anonymized data to the central entity. This boosts privacy, enables safe data for research & planning, and fosters trust in data-driven solutions. All distributed in open-source!
- Potential demonstration scenario: Barcelona strategically deployed multiple sensors along La Rambla to analyze tourist movement patterns. Key questions focused on the direction of movement and whether tourists were merely crossing the street or exploring the area. Traditionally, our cloud-based solution required centralized data storage and processing to address these questions. However, with Utip-DAM, we successfully transitioned to a distributed approach, sharing only relevant data between sensors. This shift significantly reduced processing time and enhanced data privacy.
Team

Erel Rosenberg
Erel Rosenberg has over 20 years of experience in founding and managing IT startups.

Clara (Jung Eun) Lee
Clara (Jung Eun) Lee is a data scientist. Her international background, between South Korea and the USA, makes her essential for projects that involve collecting, analysing, and interpreting data.

Thanda Oo
Thanda Oo is a lead front-end developer with more than 3 years of experience in creating visually appealing and user-friendly interfaces, developing and maintaining analytics dashboards.

Clea Rozenblum
Clea Rozenblum has over 5 years of experience in marketing for international firms. This experience gives her a strong understanding of the global marketplace and the needs of international customers.

Iris Desbrousses
Iris Desbrousses, with experiences in both France and South Korea, she is skilled at developing and executing marketing campaigns that reach and engage target audiences.
Entity

Correlation Systems
Founded in 1992, the Israeli SME Correlation Systems (14 employees, including 7 in R&D) evolved from AI situational awareness tech to its current focus on IoT sensors used by smart cities worldwide for mobility tracking and crowd safety.
Website: cs.co.il