A review of privacy-preserving human and human activity recognition

Abstract

Many automation technologies using software are making humans convenient. One of these technologies is to collect data through cameras and sensors that are common in personal life and automatically recognize human and human activities. The goal of automation is to analyze the various types of big data that are difficult to perform mechanical data mining. Raw data collected from cameras and sensors are nothing but big data before analysis. In this case, how to protect data by secure storage is the most important issue. However, when the context-aware semantic information such as a specific person and his behavior is extracted from the analysis, the security sensitivity is increased. In other words, the secondary information generated by interpreting and extracting personal location and behavioral information contained in images and videos is linked to other personal information, causing privacy infringement issues. Privacy issues become important because there is a lot of software that everyone can access. Therefore, it is necessary to study privacy protection methods in the automatic recognition of human and human activities. This paper analyzes the cutting-edge research trends, techniques, and issues of privacy-preserving human and human activity recognition.