{"id":21495,"date":"2023-02-21T00:02:00","date_gmt":"2023-02-21T00:02:00","guid":{"rendered":"https:\/\/cit.upc.edu\/?post_type=portfolio&p=21495"},"modified":"2023-07-17T06:49:41","modified_gmt":"2023-07-17T06:49:41","slug":"anem-models-and-techniques-of-data-anonymisation-with-applications-in-the-mobility-sector","status":"publish","type":"portfolio","link":"https:\/\/cit.upc.edu\/en\/portfolio-item\/anem-models-and-techniques-of-data-anonymisation-with-applications-in-the-mobility-sector\/","title":{"rendered":"ANEM: Models and techniques of data anonymisation with applications in the mobility sector"},"content":{"rendered":"\n

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21\/02\/2023
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The sharing of data between public and private companies has several barriers due to the potential threats that these organisations perceive. In all areas, but above all in mobility, the sharing and correlation of data from agents in the same ecosystem helps to improve processes within organisations and attain more sustainable mobility at global scale. To achieve these goals, it is vital to guarantee data privacy (for data of the business and the users) and data anonymisation is key to encourage data sharing.\r
In this context, the Data Anonymization Models and Techniques (ANEM) project has emerged. Participants include the Smart Services for Information Systems and Communication Networks (SISCOM) research group of the Universitat Polit\u00e8cnica de Catalunya – BarcelonaTech (UPC).\r
The aim of ANEM has been to develop, using latest generation models and techniques, a modular platform that can generate synthetic data (that is, not real data but data that can be used to train the artificial intelligence), protected from potential threats in the area of cyberspace. The distinguishing aspect developed by the UPC group is that the generation of synthetic data will provide differential privacy, that is, there will be a mathematically provable guarantee that it will not be possible to identify the users who have participated in the algorithm training data, and their privacy will be maintained.\r
The project combines experience in the area of ICT (privacy and data anonymisation, big data, artificial intelligence and cybersecurity) and in the area of sustainable mobility.\r
The SISCOM group has collaborated in this project by contributing its expertise in privacy and data anonymisation, based on models and algorithms for data generation. Subsequently, it has supported the validation of the anonymisation tool that was generated as a result of the project.
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Results and impact<\/h4>\r
The results of this project will have an impact in society on how data are obtained that can be shared in a safe, easy way. In addition, in specialised fields or sectors such as that of mobility, it could have a favourable impact to promote more efficient models that are rich in information, which will help to transform and optimise the model of mobility with a focus on citizens, economy and the environment.\r
The project was coordinated by the company Mosaic Factor, which is specialised in Big Data and artificial intelligence in the area of sustainable mobility of people and goods. Other participants in this project are the i2CAT foundation and B:SM, which provided mobility data for Barcelona.
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Budget and funding<\/h4>\r
ANEM is a technological innovation project funded by AGAUR in the INNOTEC-2020 call, with a budget of \u20ac200,000. The project was executed from June 2021 to December 2022.
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Technology<\/h5>\r
Digital Transformation<\/a>\r
Logistics and Mobility<\/a>
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Transport and Mobility<\/a>
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Mobile Technologies<\/a>
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