With smartphone usage now a global phenomenon, mobile apps and connectivity are common denominators binding people the world over. And as the world’s nations grapple with the common dilemma of how to manage the ongoing pandemic of coronavirus or COVID-19, it’s little wonder that governments and health authorities across the planet are turning to mobile app technology as a weapon in their crisis management arsenal.
In recent weeks we have been following the race to build contact tracing smartphone apps in the worldwide fight against COVID-19. Such apps are a powerful weapon in controlling the growth of infection by automating the scaling of the contact tracing process. By tracking interactions between people, the apps allow instant user notification if they have recently been in close proximity with anyone later diagnosed with COVID-19. This allows immediate social distancing or self isolation measures to be instituted for that potential infected user, slowing the spread of the virus. It would have been better if these apps were widely available during the initial phase of the pandemic, but they may still have a crucial role to play as we eventually emerge from full lockdown We have some specific suggestions about how this can be achieved while maintaining citizen anonymity.
There is much to discuss in the wake of the security news flow last week. It was dominated by the Meltdown and Spectre CPU bug announcements — 2018 has certainly got off to an interesting start. In part one of this two part blog I will look at these bugs from a high level. In part two I shine the spotlight on the implications for mobile security, and for Android in particular.
Recently I was doing some API analysis on a video sharing app aimed at the teenage market. As is typical in these types of apps, before you can do anything you need to sign up with an account. You’d think that would be straightforward enough, right?
Yesterday morning security forums reported news that an AI researcher had published a dataset of 40,000 photos that had been scraped from the dating app Tinder. The purpose was simply to extract a real world data set that can be used for training Convolutional Neural Networks (CNN) to tell the difference between men and women. This seems innocent enough, although the author's choice of variable naming caused a bit of a stir. He quickly changed the variable name "hoe" to "subject" soon after the story broke. Apparently this original naming was inherited from the Tinder Auto-Liker code.
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(Image by Nam-ho Park)
There is a revolution underway in healthcare in the USA. At its heart is MU3, Meaningful Use Stage 3 of the Electronic Health Record incentive program. One of the goals of this program is to empower patients and give them greater access to their medical records. Healthcare providers will have a legal responsibility to allow patients to access their data and they also have a responsibility to ensure the security of the data they provide. They have to walk a fine line between ease of access and security, and they have to do it by 2018.