Abstract-Human activity recognition (HAR) has become an important emerging field of application for sensor networks (SN) technologies. Nevertheless, the pervasiveness of SN in everyday life has given rise to new privacy concerns especially when mining personal sensed data in external environments. From that perspective, many research works have proposed cryptographybased techniques so as to tackle SN privacy issues, yet have costed significant degradations in computational-time efficiency. In this work, we propose a novel privacy-preserving Knn classification protocol to be used in HAR process and that is based on a novel privacy-preserving protocol that aims to assess similarity between personal recorded activities and external patterns using the cosine similarity metric. We build our proposals without any cryptographic schemes in order to provide a high efficient recognition service.
REF propose a protocol to analyze the sensed data without revealing privacy.
Novel Efficient and Privacy-Preserving Protocols for Sensor-Based Human Activity Recognition
2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld)
2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld)
Computer Science