{"content":[{"text":"Fabian Maschler et al.","bbox":{"left":0.2,"top":0.184152,"width":0.147897,"height":0.01580999999999999}},{"text":"We present a software system that automatically validates entire leak files with millions of \n entries, as well as single identity records. We cross-validate our approach on 900 manually \n labeled leak files and achieve an F1-Measure of 85 %. Experiments on a database with more \n than 180 million leaked identities detect 10 % of the identities as invalid.","bbox":{"left":0.199213,"top":0.210684,"width":0.601205,"height":0.05840999999999999}},{"text":"2","bbox":{"left":0.2,"top":0.294862,"width":0.010041999999999995,"height":0.020718000000000014}},{"text":"Related Work","bbox":{"left":0.230125,"top":0.294862,"width":0.11893499999999999,"height":0.020718000000000014}},{"text":"Jäger et al. propose a leak checker service and explain its functionality [Ja15]. They describe \n a manual process of collecting leak data from hacker forums, the Dark Web, and file hosting \n providers or BitTorrent. A basic analysis of password distributions is conducted, which has \n been previously done by Wang et al. limited to Chinese passwords [Wa15]. Butler et al. \n focus on tracing a leak and identifying its surface point [BWP16]. The authors address large \n service providers, such as PayPal, and present concepts to automatically gather leaked data \n to inform and protect customers. Involving attacked service providers, Nixon summarizes \n several ideas on vetting leaks [Ni14]. According to that, password policies or measures for \n uniqueness and existence of usernames and email addresses are useful for the validation of \n leak data. Other ideas include checking the formatting of credit card numbers or analyzing \n the distribution of real names. Unfortunately, Nixons summary is missing an evaluation of \n the described ideas with real data. With our work, we fill this gap with experiments on a \n real-world dataset and moreover extend the list of effective and efficient validity checks. \n Recently, our results have been incorporated into the ILC and build the basis for ongoing \n identity leak research of Jäger and Graupner et al. [Gr16,Ja16].","bbox":{"left":0.199682,"top":0.327837,"width":0.603242,"height":0.21461500000000006}},{"text":"3","bbox":{"left":0.2,"top":0.568218,"width":0.010041999999999995,"height":0.020718000000000014}},{"text":"Leak Validation","bbox":{"left":0.230125,"top":0.568218,"width":0.13723100000000002,"height":0.020718000000000014}},{"text":"Our software system decomposes identity leak validation into two subtasks: (i) Validation \n of a leak file in its entirety based on features of the full dataset and (ii) Validation of a single \n identity and its username, email address, etc. in detail. Table 1 lists all proposed validation \n methods.","bbox":{"left":0.2,"top":0.601194,"width":0.600004,"height":0.05841200000000002}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}