"Is Your Smart Watch Sharing Your Data?"

Users may be unaware of the exchange of data that household Internet of Things (IoT) devices, such as the Ring doorbell, Peloton exercise bike, and Nest thermostat conduct with other devices and systems over the network. These devices store various types of information about a user that could be considered highly private such as their height, weight, and schedule. IoT manufacturers use data to improve their future products. However, users want to be assured about the security of their private information. Missouri S&T researchers propose the improvement of the Machine Learning (ML) technique, federated learning, to maintain the accuracy of IoT-collected data, while protecting the data from attacks or invasions of privacy. They are designing new federated learning algorithms with data privacy and accuracy in mind. This article continues to discuss companies' use of IoT-collected data to improve their products, the importance of securing this data, the concept of federated learning, and the development of new federated learning algorithms to preserve the accuracy, privacy, and security of IoT data. 

Missouri S&T reports "Is Your Smart Watch Sharing Your Data?"