TY - GEN
T1 - User-centred privacy inference detection for smart home devices
AU - Kounoudes, Alexia Dini
AU - Kapitsaki, Georgia M.
AU - Katakis, Ioannis
AU - Milis, Marios
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In the smart home, vast amounts of data are being collected via various interconnected devices. Although this assists in improving the quality of life at home, often the user is not aware of the details concerning data collection apart from the information available on the provider privacy policy. It is however important to put the user inside this loop of information, so that she is well informed on possible uses of the data and the potential risks that this may entail. Previous works have identified user activity inside the smart home and have pointed out privacy threats. In this work, we go one step further by offering data inference techniques and giving this information back to the user. We use a number of machine learning techniques to draw conclusions about the user routines or activities and we inform the user about our findings concerning data inferences through a dedicated web application. Our aim is toward user-centred privacy and is a proof of concept that can be reused by smart home and Internet of Things service providers in general in order to improve the services offered to the end-users. Our results indicate that a large number of data inferences are possible by using a combination of techniques.
AB - In the smart home, vast amounts of data are being collected via various interconnected devices. Although this assists in improving the quality of life at home, often the user is not aware of the details concerning data collection apart from the information available on the provider privacy policy. It is however important to put the user inside this loop of information, so that she is well informed on possible uses of the data and the potential risks that this may entail. Previous works have identified user activity inside the smart home and have pointed out privacy threats. In this work, we go one step further by offering data inference techniques and giving this information back to the user. We use a number of machine learning techniques to draw conclusions about the user routines or activities and we inform the user about our findings concerning data inferences through a dedicated web application. Our aim is toward user-centred privacy and is a proof of concept that can be reused by smart home and Internet of Things service providers in general in order to improve the services offered to the end-users. Our results indicate that a large number of data inferences are possible by using a combination of techniques.
KW - Inference detection
KW - Privacy
KW - Smart home
KW - User-centred privacy
UR - http://www.scopus.com/inward/record.url?scp=85123303371&partnerID=8YFLogxK
U2 - 10.1109/SWC50871.2021.00037
DO - 10.1109/SWC50871.2021.00037
M3 - Conference contribution
AN - SCOPUS:85123303371
T3 - Proceedings - 2021 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People, and Smart City Innovations, SmartWorld/ScalCom/UIC/ATC/IoP/SCI 2021
SP - 210
EP - 218
BT - Proceedings - 2021 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People, and Smart City Innovations, SmartWorld/ScalCom/UIC/ATC/IoP/SCI 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People, and Smart City Innovations, SmartWorld/ScalCom/UIC/ATC/IoP/SCI 2021
Y2 - 18 October 2021 through 21 October 2021
ER -