Possible Solution to Privacy Erosion in COVID-19 Age
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עברית (Hebrew)
Location technology has emerged as an integral part of COVID-19 response globally. Smartphones and citizen data are used globally by authorities to identify possible infection outbreaks, to deliver emergency services in hotspots, or to keep people indoors.
With this development, concerns over data safety and privacy erosion have grown. In order to address these challenges, it is important for government, organizations and technology players to collaborate together and help inform people’s understanding of privacy while enabling them to make informed choices, regarding their data and to protect it from potential mischief-makers, according to cnbctv18.com.
Most contact tracing apps and location-based tools rely on mobile technologies such as Bluetooth to identify physical distance and when people meet, based on the strength of the Bluetooth radio signal. Bluetooth can detect another phone within its vicinity, but it cannot pinpoint the direction that person is coming from. It does not provide a timely context either. Federated learning (FL) is one potential avenue in machine learning that can address this challenge, while still giving those entities a degree of control and privacy. This is because FL removes the need for end-users and businesses never to transfer their data to external third parties. FL trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them.
At present, a great deal of mobile data is directly being collected by companies and governments through the apps we use, and the information goes to a data center in the cloud. The company that owns the data can analyze it to create new models of business and produce fresh insights into people’s behavior. This contentious data model raises current security and privacy concerns.
With Federated learning, there is an alternative transfer of model to the user, where the data is collected locally. As this happens, only the derived knowledge is shared back to the cloud, without personal information, adding an additional layer of security, while the other relevant information can be used to develop and improve the cloud-based service. In other words, the smartphone turns into an ‘edge sensor’ providing only the data close to the edge of the areas that we are examining.
However, Federated learning is known to be vulnerable to backdoor and inference attacks. To protect against these known weaknesses, federated learning should be combined with Differential Privacy or other available techniques, where necessary.
Although location technology promises unlimited possibilities, the governments and technology companies have a mandate to balance these concerns, at both personal and enterprise-level, in ways that are respectful and accommodating of people’s fears around security compromise, data ownership and the ability to control their own data. This is where FL offers a promising future, as it is putting the privacy of the user at the center.
