Data privacy remains to be one of the topmost concerns for everyone. Some of our personal or sensitive information is being collected from us even without our knowledge. Although Machine learning comes with a great number of advantages, it also comes with a great number of risks. It is interesting to note that Machine Learning is being used in internet search engines to help us get the most relevant results, it is also used in email filters to sort out spam, and websites to make personalized recommendations. Banking software is also using ML to detect unusual transactions and many more.
As you can see, Machine Learning is indeed hungry for data. However, despite the overwhelming number of benefits you can get from Machine learning, our privacy is often being compromised. Interestingly, in this video presented by Catherine Nelson from the recently concluded PyCon 2020, we will learn that it is possible to build an accurate machine learning model while still preserving user privacy. According to the video, there is a great number of tools in Python to help us build a machine learning model without compromising user privacy. Feel free to watch the video below and learn more about privacy-preserving machine learning in Python.
