Personalized Differential Privacy Preserving Data Aggregation for Smart Homes
Xin-Yuan Zhang, Liu-Sheng Huang, Shao-Wei Wang, Zhen-Yu Zhu, Hong-Li Xu
Available Online December 2016.
- https://doi.org/10.2991/icwcsn-16.2017.45How to use a DOI?
- smart homes; aggregation; differential privacy; local; personalized
- The aggregation of residents' private data drives improvements in the smart homes, however it comes with compromising on privacy. Hence, privacy preservation has become an increasing requirement for residents. Since users might have different privacy requirements, and their privacy requirements might be sensitive information, smart homes need a privacy preservation scheme to meet their demands. In this scheme, a user preserves the privacy of his/her data and privacy level locally by specifying his own privacy level in confidence, without trusting anyone else in smart homes. In addition, a user replies a single data element to the collector each time, instead of the whole dataset. It makes the scheme more challenging than the traditional centralized situation. In this paper, we propose a novel personalized local differential privacy preservation scheme for smart homes, which retains desirable utility while providing rigorous privacy guarantee.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Xin-Yuan Zhang AU - Liu-Sheng Huang AU - Shao-Wei Wang AU - Zhen-Yu Zhu AU - Hong-Li Xu PY - 2016/12 DA - 2016/12 TI - Personalized Differential Privacy Preserving Data Aggregation for Smart Homes PB - Atlantis Press SP - 203 EP - 209 SN - 2352-538X UR - https://doi.org/10.2991/icwcsn-16.2017.45 DO - https://doi.org/10.2991/icwcsn-16.2017.45 ID - Zhang2016/12 ER -