Brief look at Data Privacy, Personalized ad IDs and ML
The idea of data privacy trickles down to how, as individuals, we are working towards keeping our data safe. This is, however, a challenge to most. In an era where almost, everyone is on social media platforms and where almost everyone is compelled to share their life there, it might be hard to ensure one’s privacy. Data regulation policies have been implemented to ensure that the platforms we share our data on safeguard the data and keep our privacy. But is this the case?
While data regulation policies focus on making data usage clear, organizations have found loopholes to use various activities. Still, they remain under the regulations of these data regulation policies. Social media platforms collect data and use it to personalize ads. Yes, you can choose not to have personalized ads by disabling creating an ad ID on your Microsoft device, but that does not mean that you won’t get ads. You will still get them only that they won’t be entirely relevant to you.
There are certain aspects of privacy that need to be ensured. One can be focused on retaining their privacy by focusing on such approaches. One is on the differential privacy. Under the idea of differential privacy, one can collect data and analyze but relating that data to the owner become a challenge. This would be a data privacy measure that favors the data owner. In machine learning, the output from a model should have differential privacy to protect the data owner. Anyone who views the output would not be able to tell whether certain individuals contributed their data to the model. But that is just a machine learning area.
A concern on how one share their data is on how the data will be used. Anyone can contribute their data as long as it is aimed at helping them. This mostly pertains to the health industry. While people can willingly provide their genome information to help with research to identify which health concern runs in their family, they might have reservations if the data is not clear. Insurance companies can use sane data to set insurance rates. This makes people stringent on sharing their data freely.
But is sharing data a mistake?
Think of the current pandemic and the steps taken by Google in enabling contact tracing to identify people who have come across infected parties? The field of data collection and tracking can help inform people whether someone they came across has been infected, thereby helping them take measures to ensure their health. However, the same data can be wrongly used if this is not protected. However, most people were angered by the idea that the contact tracking feature was added without their consent. However, this raises the tension on the trade-off between personal privacy and public health. It is a call for social and the regulation policymakers to decide how to approach this issue. Should the side taken be on public health protection or privacy protection?
<to be continued>
