Privacy-preserving data analytics
Yang Zhao · 2022
Massive volumes of sensitive information are being collected for data analytics and machine learning, such as large-scale Internet of Things (IoT) data.Some IoT data contain users' confidential information, for example, energy consumption or location data.These data may expose a family's habits and routines that attackers may utilize to perform attacks [1-5].The Internet of Vehicles (IoV), a promising branch of IoT, simulates a large variety of crowdsourcing applications such as Waze, Uber, and Amazon Mechanical Turk.These applications report the real-time traffic information to the cloud server, which trains a machine learning model based on traffic information uploaded by intelligent traffic management users.However, crowdsourcing application owners can easily infer users' location information, traffic information, motor vehicle information, and environmental information, etc., raising severe sensitive personal information privacy concerns.Besides, as the number of vehicles increases, the frequent communication between vehicles and the cloud server incurs a tremendous communication cost.Many countries have strict policies, regulations, and laws on how technology companies collect and process users' data to protect personal privacy.These companies need to analyze users' data to improve their service quality.In order to preserve