%0 Conference Paper %1 10.1145/3430984.3431028 %A Mukherjee, Avirup %A Mallick, Madhumita %A Chakraborty, Sandip %A Ganguly, Niloy %B 8th ACM IKDD CODS and 26th COMAD %C New York, NY, USA %D 2021 %I Association for Computing Machinery %K leibnizailab myown %P 193–197 %R 10.1145/3430984.3431028 %T Unsupervised Topology Assessment in Smart Homes %U https://doi.org/10.1145/3430984.3431028 %X Nowadays, a wide range of IOT devices are deployed in a variety of environments and settings to enhance the quality of human life. With a huge amount of data being generated from them, privacy is becoming a very big concern. To determine the level of privacy breach that can be achieved, we introduce in this paper, an unsupervised approach to visualize the sensor network, which in turn divulges the indoor topology of a smart home. The results are obtained from a smart environment by conducting a series of deductions and analysis on sensor datasets generated by a smart home. The experimental results demonstrate that our approach is able to deduce room-level sensor topology for a smart home even without the knowledge of any activity label or any prior information about the environment. %@ 9781450388177

@inproceedings{10.1145/3430984.3431028, abstract = { Nowadays, a wide range of IOT devices are deployed in a variety of environments and settings to enhance the quality of human life. With a huge amount of data being generated from them, privacy is becoming a very big concern. To determine the level of privacy breach that can be achieved, we introduce in this paper, an unsupervised approach to visualize the sensor network, which in turn divulges the indoor topology of a smart home. The results are obtained from a smart environment by conducting a series of deductions and analysis on sensor datasets generated by a smart home. The experimental results demonstrate that our approach is able to deduce room-level sensor topology for a smart home even without the knowledge of any activity label or any prior information about the environment.}, added-at = {2022-02-24T21:38:48.000+0100}, address = {New York, NY, USA}, author = {Mukherjee, Avirup and Mallick, Madhumita and Chakraborty, Sandip and Ganguly, Niloy}, biburl = {https://www.bibsonomy.org/bibtex/2583a50889d56e9cc8102d6725186f86b/niloy}, booktitle = {8th ACM IKDD CODS and 26th COMAD}, doi = {10.1145/3430984.3431028}, interhash = {12f69e6bb4de282e1353edffc1bfafc1}, intrahash = {583a50889d56e9cc8102d6725186f86b}, isbn = {9781450388177}, keywords = {leibnizailab myown}, location = {Bangalore, India}, numpages = {5}, pages = {193–197}, publisher = {Association for Computing Machinery}, series = {CODS COMAD 2021}, timestamp = {2022-02-25T09:01:40.000+0100}, title = {Unsupervised Topology Assessment in Smart Homes}, url = {https://doi.org/10.1145/3430984.3431028}, year = 2021 }