<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

A review of privacy-preserving human and human activity recognition

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

running-head

Many automation technologies using software are making humans convenient. One of these technologies is to collect data through cameras and sensors that are...

A review of privacy-preserving human and human activity recognition

Article Category: Research-Article

Publicado en línea: 25 may 2020

Páginas: 1 - 13

Recibido: 30 mar 2020

DOI: https://doi.org/10.21307/ijssis-2020-008

Palabras clave
Human recognition, Human activity recognition, Privacy protection, Machine learning

© 2020 Im Y. Jung published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A review of privacy-preserving human and human activity recognition

Article Category: Research-Article

Publicado en línea: 25 may 2020

Páginas: 1 - 13

Recibido: 30 mar 2020

DOI: https://doi.org/10.21307/ijssis-2020-008

Palabras claveHuman recognition, Human activity recognition, Privacy protection, Machine learning

© 2020 Im Y. Jung published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Palabras clave
Human recognition, Human activity recognition, Privacy protection, Machine learning