The Experts below are selected from a list of 7293 Experts worldwide ranked by ideXlab platform
Sarah Kern - One of the best experts on this subject based on the ideXlab platform.
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Fusion of Mobile Device Signal Data Attributes Enables Multi-Protocol Entity Resolution and Enhanced Large-Scale Tracking
arXiv: Signal Processing, 2019Co-Authors: Brian Thompson, Dave Cedel, Jeremy Martin, Peter Ryan, Sarah KernAbstract:Use of persistent identifiers in wireless communication Protocols is a known privacy concern as they can be used to track the location of mobile devices. Furthermore, inherent structure in the assignment of hardware identifiers as well as upper-layer network Protocol data attributes can leak additional device information. We introduce SEXTANT, a computational framework that combines improvements on previously published device identification techniques with novel spatio-temporal correlation algorithms to perform multi-Protocol Entity resolution, enabling large-scale tracking of mobile devices across Protocol domains. Experiments using simulated data representing Las Vegas residents and visitors over a 30-day period, consisting of about 300,000 multi-Protocol mobile devices generating over 200 million sensor observations, demonstrate SEXTANT's ability to perform effectively at scale while being robust to data heterogeneity, sparsity, and noise, highlighting the urgent need for the adoption of new standards to protect the privacy of mobile device users.
Brian Thompson - One of the best experts on this subject based on the ideXlab platform.
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Fusion of Mobile Device Signal Data Attributes Enables Multi-Protocol Entity Resolution and Enhanced Large-Scale Tracking
arXiv: Signal Processing, 2019Co-Authors: Brian Thompson, Dave Cedel, Jeremy Martin, Peter Ryan, Sarah KernAbstract:Use of persistent identifiers in wireless communication Protocols is a known privacy concern as they can be used to track the location of mobile devices. Furthermore, inherent structure in the assignment of hardware identifiers as well as upper-layer network Protocol data attributes can leak additional device information. We introduce SEXTANT, a computational framework that combines improvements on previously published device identification techniques with novel spatio-temporal correlation algorithms to perform multi-Protocol Entity resolution, enabling large-scale tracking of mobile devices across Protocol domains. Experiments using simulated data representing Las Vegas residents and visitors over a 30-day period, consisting of about 300,000 multi-Protocol mobile devices generating over 200 million sensor observations, demonstrate SEXTANT's ability to perform effectively at scale while being robust to data heterogeneity, sparsity, and noise, highlighting the urgent need for the adoption of new standards to protect the privacy of mobile device users.
Peter Ryan - One of the best experts on this subject based on the ideXlab platform.
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Fusion of Mobile Device Signal Data Attributes Enables Multi-Protocol Entity Resolution and Enhanced Large-Scale Tracking
arXiv: Signal Processing, 2019Co-Authors: Brian Thompson, Dave Cedel, Jeremy Martin, Peter Ryan, Sarah KernAbstract:Use of persistent identifiers in wireless communication Protocols is a known privacy concern as they can be used to track the location of mobile devices. Furthermore, inherent structure in the assignment of hardware identifiers as well as upper-layer network Protocol data attributes can leak additional device information. We introduce SEXTANT, a computational framework that combines improvements on previously published device identification techniques with novel spatio-temporal correlation algorithms to perform multi-Protocol Entity resolution, enabling large-scale tracking of mobile devices across Protocol domains. Experiments using simulated data representing Las Vegas residents and visitors over a 30-day period, consisting of about 300,000 multi-Protocol mobile devices generating over 200 million sensor observations, demonstrate SEXTANT's ability to perform effectively at scale while being robust to data heterogeneity, sparsity, and noise, highlighting the urgent need for the adoption of new standards to protect the privacy of mobile device users.
Jeremy Martin - One of the best experts on this subject based on the ideXlab platform.
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Fusion of Mobile Device Signal Data Attributes Enables Multi-Protocol Entity Resolution and Enhanced Large-Scale Tracking
arXiv: Signal Processing, 2019Co-Authors: Brian Thompson, Dave Cedel, Jeremy Martin, Peter Ryan, Sarah KernAbstract:Use of persistent identifiers in wireless communication Protocols is a known privacy concern as they can be used to track the location of mobile devices. Furthermore, inherent structure in the assignment of hardware identifiers as well as upper-layer network Protocol data attributes can leak additional device information. We introduce SEXTANT, a computational framework that combines improvements on previously published device identification techniques with novel spatio-temporal correlation algorithms to perform multi-Protocol Entity resolution, enabling large-scale tracking of mobile devices across Protocol domains. Experiments using simulated data representing Las Vegas residents and visitors over a 30-day period, consisting of about 300,000 multi-Protocol mobile devices generating over 200 million sensor observations, demonstrate SEXTANT's ability to perform effectively at scale while being robust to data heterogeneity, sparsity, and noise, highlighting the urgent need for the adoption of new standards to protect the privacy of mobile device users.
Dave Cedel - One of the best experts on this subject based on the ideXlab platform.
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Fusion of Mobile Device Signal Data Attributes Enables Multi-Protocol Entity Resolution and Enhanced Large-Scale Tracking
arXiv: Signal Processing, 2019Co-Authors: Brian Thompson, Dave Cedel, Jeremy Martin, Peter Ryan, Sarah KernAbstract:Use of persistent identifiers in wireless communication Protocols is a known privacy concern as they can be used to track the location of mobile devices. Furthermore, inherent structure in the assignment of hardware identifiers as well as upper-layer network Protocol data attributes can leak additional device information. We introduce SEXTANT, a computational framework that combines improvements on previously published device identification techniques with novel spatio-temporal correlation algorithms to perform multi-Protocol Entity resolution, enabling large-scale tracking of mobile devices across Protocol domains. Experiments using simulated data representing Las Vegas residents and visitors over a 30-day period, consisting of about 300,000 multi-Protocol mobile devices generating over 200 million sensor observations, demonstrate SEXTANT's ability to perform effectively at scale while being robust to data heterogeneity, sparsity, and noise, highlighting the urgent need for the adoption of new standards to protect the privacy of mobile device users.
