In an epidemic, can we balance personal privacy and public safety?
- There are now 1.4 billion mobile devices in use in China, which means a lot of data that can be used to manage an infectious disease outbreak. Around the world, the use of big data is also becoming common in crisis relief, but fears about misuse remain
In times of epidemics, how are we to strike a balance between protecting personal privacy and maintaining public safety?
Today, more than five billion people in the world have mobile devices, and many of them can’t live without their phones. They represent a huge data pool that many researchers could tap to help manage an infectious disease outbreak, or locate trapped and injured individuals after an earthquake.
In 2017, GSMA, the association representing mobile operators worldwide, launched a “Big Data for Social Good” initiative that encourages telecoms groups to support responses to epidemics and natural disasters by sharing anonymised metadata.
Now, experts such as Li Tie, chairman and chief economist of the China Centre for Urban Development, have advocated the use of big data to manage a crisis and reduce the risk of a future crisis.
Coronavirus boosts China’s big data push but privacy still a concern
At the grass-roots level, some local authorities allowed only one person per family to leave the house for two hours a day. As Li pointed out in an article this month, all these drastic measures were “very effective in preventing the further spread of the epidemic, but at huge social and economic costs”.
These signals are regularly sent and received by a mobile phone each time it passes a base station in a telecoms network, but only if the phone is switched on.
There are three major mobile operators in China. If the authorities gather mobile phone signalling data from these operators, it is possible to monitor the whereabouts of the national population, or target particular regions or individuals round the clock within the country.
This means that people who have been exposed to a virus and placed on home quarantine should not be able to escape detection. In theory, it is possible to contain an epidemic without shutting down factories or an entire society.
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In another case, researchers from the Massachusetts Institute of Technology created several models to trace the spread of dengue, a mosquito-borne virus, in Singapore. The model that used the anonymised call records of 2.3 million people from 2011 was found to be more effective.
Moreover, studies have shown that even when data is anonymised and aggregated, it is possible to re-identify an individual using just four data points.
This is why Nathaniel Raymond, from the Yale Jackson Institute for Global Affairs, has warned that mobile phone data might be improperly used by public and private organisations. If the data falls into the wrong hands, people in need of asylum, for example, might be victimised by human-trafficking groups. Therefore, comprehensive guidelines must be developed on the use of mobile data in crisis relief.
One reason for the failure is the problem with big data analysis itself: analysis models are based on assumptions, and biased assumptions will generate biased results.
Most of us are accustomed to treating mobile phones like extensions of our individual selves. However, in Sierra Leone, mobile phones are “loaned, traded, and passed around among family and friends, like clothes, books, and bicycles,” according to Erikson. “A single phone can be shared by an extended family or, in rural areas, a neighbourhood or a village.” Therefore, an analysis model based on the assumption that a mobile phone is an extension of an individual was never going to work.
But what lessons can we learn from all this in Hong Kong? If Hong Kong is to become a truly smart city, the government should seize the opportunity thrown up by the outbreak and persuade telecoms operators to share big data with researchers.
But at the same, the authorities must develop comprehensive guidelines on the use of such data, to lay a solid foundation for smart health care in Hong Kong.
Dr Winnie Tang is adjunct professor in the Department of Computer Science, Faculty of Engineering and Faculty of Architecture, at the University of Hong Kong