An investigation of geographic mapping techniques for internet hosts

RIPE IPmap active geolocation

RIPE IPmap is a multi-engine geolocation platform operated by the RIPE NCC. One of its engines, single-radius, uses active geolocation to infer the geographic coordinates of target IP addresses. In this paper, we first introduce the methodology of IPmap's single-radius engine, then we evaluate its accuracy, coverage, and consistency, and compare its results with commercial geolocation databases. We found that 79.5% of single-radius results have city-level accuracy for our ground truth dataset, and 87.0% have city-level consistency or geolocating different interfaces on the same routers. On our coverage evaluation dataset of 26,559 core infrastructure IP addresses, single-radius provided geolocation inferences for 78.5% of them.We offer recommendations to improve the single-radius engine and IPmap platform in general. The main contributions of this paper are to introduce and evaluate the IPmap single-radius engine and to provide a generalized evaluation workflow applicable to historical and future geolocation techniques.

Server Location Verification (SLV) and Server Location Pinning

We introduce the first known mechanism providing realtime server location verification. Its uses include enhancing server authentication by enabling browsers to automatically interpret server location information. We describe the design of this new measurement-based technique, Server Location Verification (SLV), and evaluate it using PlanetLab. We explain how SLV is compatible with the increasing trends of geographically distributed content dissemination over the Internet, without causing any new interoperability conflicts. Additionally, we introduce the notion of (verifiable) server location pinning (conceptually similar to certificate pinning) to support SLV, and evaluate their combined impact using a server-authentication evaluation framework. The results affirm the addition of new security benefits to the existing TLS-based authentication mechanisms. We implement SLV through a location verification service, the simplest version of which requires no server-side changes. We also implement a simple browser extension that interacts seamlessly with the verification infrastructure to obtain realtime server location-verification results.

Properties of healthcare teaming networks as a function of network construction algorithms

Network models of healthcare systems can be used to examine how providers collaborate, communicate, refer patients to each other, and to map how patients traverse the network of providers. Most healthcare service network models have been constructed from patient claims data, using billing claims to link a patient with a specific provider in time. The data sets can be quite large (106-108 individual claims per year), making standard methods for network construction computationally challenging and thus requiring the use of alternate construction algorithms. While these alternate methods have seen increasing use in generating healthcare networks, there is little to no literature comparing the differences in the structural properties of the generated networks, which as we demonstrate, can be dramatically different. To address this issue, we compared the properties of healthcare networks constructed using different algorithms from 2013 Medicare Part B outpatient claims data. Three different algorithms were compared: binning, sliding frame, and trace-route. Unipartite networks linking either providers or healthcare organizations by shared patients were built using each method. We find that each algorithm produced networks with substantially different topological properties, as reflected by numbers of edges, network density, assortativity, clustering coefficients and other structural measures. Provider networks adhered to a power law, while organization networks were best fit by a power law with exponential cutoff. Censoring networks to exclude edges with less than 11 shared patients, a common de-identification practice for healthcare network data, markedly reduced edge numbers and network density, and greatly altered measures of vertex prominence such as the betweenness centrality. Data analysis identified patterns in the distance patients travel between network providers, and a striking set of teaming relationships between providers in the Northeast United States and Florida, likely due to seasonal residence patterns of Medicare beneficiaries. We conclude that the choice of network construction algorithm is critical for healthcare network analysis, and discuss the implications of our findings for selecting the algorithm best suited to the type of analysis to be performed.