National Statistics, Education and Transport
6 Use Cases
Eurostat, a Directorate-General of the European Commission, has explored the use of Mobile Network Operator (MNO) data for the creation of official statistics on human population movement and mobility. The project analysed data in a trusted execution environment provided by Cybernetica’s Sharemind technology to ensure that no individual data was shared, and no individuals could be identified. The product was tested using synthetic data on a population size of up to 100 million, aimed at demonstrating the scalability of the project. In parallel, research was conducted to assess the legal dimensions of the data processing in the project. Eurostat found that the project suggested the rising potential for the use of PETs in creating official statistics.
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Statistics Canada, National Statistics Office of Canada, has piloted the use of synthetic data to supply hackathon participants with data containing high analytical value. The risk of using real Statistics Canada data was clear, as many relevant resources are census datasets and mortality registries which clearly relate to personally identifiable individuals. Statistics Canada held two hackathons executed in this way and observed that the analytical skills of the participants were improved, while reducing the risk of disclosure. This means that using synthetic data can have significant positive impact on training scenarios, whilst preserving the privacy of data subjects.
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Statistics Korea, the National Statistics Office of Korea, has begun trying to improve the linking of fragmented government data through a public cloud-based big data system named the “Statistical Data Hub Platform”. The provenance of this data stretches across government departments and so it may have significant utility for range of stakeholders operating in different contexts. However, accordingly, certain data may be sensitive and/or reflect identifiable data subjects. Statistics Korea has piloted the use of multiple PETs for this purpose. For example, the pilot involved links small business data, which is encrypted by using homomorphic encryption.
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| A pilot programme by the newly launched UN PET Lab is exploring how to improve the understanding of international trade using privacy enhancing technologies. The programme was announced in January 2022 and pilots the use of several PETs to produce useful statistics without sharing or compromising the input data. The example applications include verification of import and export quantities between countries by comparing the statistics that ‘paired’ countries hold on to how much they have sold and bought of a given commodity from the other. This insight is created by the use of multi-party computation and differential privacy, via a peer-to-peer differential data network created by OpenMined. The initiative also involved creation of an enclave technology provided by Oblivious, which hosts analysis in a secure trusted execution environment, such that only query outputs are shared. The technology also ensures that the output cannot be modified after creation. National statistics offices from the UK, the Netherlands, Italy and Canada took part in the programme, which initially used data uploaded on the UN Comtrade portal. A broader objective of this UN PET Lab project is to build an understanding how international data sharing can be improved using privacy enhancing technologies, as the technologies used in this example can be applied for other forms of data. |
The Estonian Center of Applied Research (CentAR) used multi-party computation to carry out a big data study on the association between students, working during their studies at university, and whether they graduated in time. The project was the largest example of a statistical study on real data, using encryption for data privacy, to date when it was completed in 2015. 10 million tax records were linked to 60,000 education records from a Ministry of Education database by using Sharemind encryption tools, operated by the data owners, which meant that the data was never unecrypted outside of where it was originally stored. CentAR led the study, which was instigated by the Estonian Association of Information and Technology and Telecommunications. The Sharemind MPC system was hosted by the Estonian Information System’s Authority, the Information Technology Centre of the Ministry of Finance, and private organisation, Cybernetica. This study demonstrates the use of MPC for building policy insights with large datasets and high levels of precision, while protecting personal data.
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In response to the Indian government increasing research into unmanned aerial vehicles (UAVs) and remotely piloted vehicles (RPVs), the Indian Institute of Science’s (IISc) has developed “Privaros.” This is a set of enhancements to the drone software stack and designed to mitigate privacy concerns around the many sensors, cameras, microphones and GPS capabilities drones are equipped with. Privaros allows a host airspace, such as apartment complexes, university campuses and city municipalities, to determine their own privacy policies and ensure commercial delivery drones are compliant. The working prototype is equipped with a hardware trusted execution environment (TEE) unlike most off-the-shelf drones. Evaluation shows that a drone running Privaros can robustly enforce various privacy policies specified by hosts with only marginal increases to communication latency and power consumption.