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Privacy Protection Method based on Multi-Object Authentication in Intelligent CCTV Environment
http://doi.org/10.5626/JOK.2019.46.2.154
In the intelligent CCTV surveillance environment, personal identity is confirmed based on face recognition. However, the recognition rate of the current face recognition technology is still faulty. In particular, face recognition may not work correctly due to various causes such as CCTV shot quality, weather, personal pose and facial expression, hairstyle, lighting condition, and so on. In this case, there is a great risk of exposing object`s privacy information in the video surveillance environment due to erroneous object judgment. The proposed method can increase the recognition rate of objects based on the CCTV-RFID hybrid authentication method, and thus protect the privacy of the image object.
Design of EEG Signal Security Scheme based on Privacy-Preserving BCI for a Cloud Environment
Kwon Cho, Donghyeok Lee, Namje Park
http://doi.org/10.5626/JOK.2018.45.1.45
With the advent of BCI technology in recent years, various BCI products have been released. BCI technology enables brain information to be transmitted directly to a computer, and it will bring a lot of convenience to life. However, there is a problem with information protection. In particular, EEG data can raise issues about personal privacy. Collecting and analyzing big data on EEG reports raises serious concerns about personal information exposure. In this paper, we propose a secure privacy-preserving BCI model in a big data environment. The proposed model could prevent personal identification and protect EEG data in the cloud environment.
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