Scientists have found that people's privacy is at high risk as there is a growing practice of compiling massive, anonymised datasets about their movement patterns, mentioned a report.
The researchers from Massachusetts Institute of Technology (MIT) made the first study on "matchability" of user mobility by analysing two large-scale datasets from a mobile network operator and a local transportation system in Singapore, Xinhua news agency reported on Saturday.
According to the report, the mobile data contained timestamps and geographic coordinates in over 485 million records from over 2 million users, while the transportation data comprised over 70 million records with timestamps for individuals moving through the city.
They found data containing "location stamps" -- information with geographical coordinates and time stamps -- could be used to easily track the mobility trajectories of how people live and work.
Those sensitive data can be obtained from mobile phone records, credit card transactions, public transportation smart cards, social media accounts and other mobile apps, said the MIT study published on Friday in IEEE Transactions on Big Data.
In the MIT experiments, the researchers could match about 17 per cent of individuals in one week's worth of data, and more than 55 per cent of individuals after one month of collected data. The accuracy rate jumped to about 95 per cent with data compiled over 11 weeks.
"All data with location stamps is potentially very sensitive... We need to keep thinking about the challenges in processing large-scale data, about individuals, and the right way to provide adequate guarantees to preserve privacy," said the study's co-author Carlo Ratti, a professor at the MIT Department of Urban Studies and Planning.
The researchers hope their study can increase public awareness of protecting their privacy and promote tighter regulations for sharing consumer data by companies, the academic community and other entities.
(This story has not been edited by Business Standard staff and is auto-generated from a syndicated feed.)