Privacy Issues of the W3C Geolocation API

The W3C's Geolocation API may rapidly standardize the transmission of location information on the Web, but, in dealing with such sensitive information, it also raises serious privacy concerns. We analyze the manner and extent to which the current W3C Geolocation API provides mechanisms to support privacy. We propose a privacy framework for the consideration of location information and use it to evaluate the W3C Geolocation API, both the specification and its use in the wild, and recommend some modifications to the API as a result of our analysis

Finding and Analyzing Evil Cities on the Internet

IP Geolocation is used to determine the geographical location of Internet users based on their IP addresses. When it comes to security, most of the traditional geolocation analysis is performed at country level. Since countries usually have many cities/towns of different sizes, it is expected that they behave differently when performing malicious activities. Therefore, in this paper we refine geolocation analysis to the city level. The idea is to find the most dangerous cities on the Internet and observe how they behave. This information can then be used by security analysts to improve their methods and tools. To perform this analysis, we have obtained and evaluated data from a real-world honeypot network of 125 hosts and from production e-mail servers

Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks

We propose a method for embedding two-dimensional locations in a continuous vector space using a neural network-based model incorporating mixtures of Gaussian distributions, presenting two model variants for text-based geolocation and lexical dialectology. Evaluated over Twitter data, the proposed model outperforms conventional regression-based geolocation and provides a better estimate of uncertainty. We also show the effectiveness of the representation for predicting words from location in lexical dialectology, and evaluate it using the DARE dataset.Comment: Conference on Empirical Methods in Natural Language Processing (EMNLP 2017) September 2017, Copenhagen, Denmar