The UN is Testing Technology That Processes Data Confidentially

How to analyse data without revealing their secrets? From a report: Data are valuable. But not all of them are as valuable as they could be. Reasons of confidentiality mean that many medical, financial, educational and other personal records, from the analysis of which much public good could be derived, are in practice unavailable. A lot of commercial data are similarly sequestered. For example, firms have more granular and timely information on the economy than governments can obtain from surveys. But such intelligence would be useful to rivals. If companies could be certain it would remain secret, they might be more willing to make it available to officialdom. A range of novel data-processing techniques might make such sharing possible. These so-called privacy-enhancing technologies (PETs) are still in the early stages of development. But they are about to get a boost from a project launched by the United Nations' statistics division. The UN PETs Lab, which opened for business officially on January 25th, enables national statistics offices, academic researchers and companies to collaborate to carry out projects which will test various PETs, permitting technical and administrative hiccups to be identified and overcome. The first such effort, which actually began last summer, before the PETs Lab's formal inauguration, analysed import and export data from national statistical offices in America, Britain, Canada, Italy and the Netherlands, to look for anomalies. Those could be a result of fraud, of faulty record keeping or of innocuous re-exporting. For the pilot scheme, the researchers used categories already in the public domain -- in this case international trade in things such as wood pulp and clocks. They thus hoped to show that the system would work, before applying it to information where confidentiality matters. They put several kinds of PETs through their paces. In one trial, OpenMined, a charity based in Oxford, tested a technique called secure multiparty computation (SMPC). This approach involves the data to be analysed being encrypted by their keeper and staying on the premises. The organisation running the analysis (in this case OpenMined) sends its algorithm to the keeper, who runs it on the encrypted data. That is mathematically complex, but possible. The findings are then sent back to the original inquirer.

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