مقاله انگلیسی رایگان در مورد آنالیز اطلاعات انگشت نگاری کردن کلان داده ها برای پایداری – الزویر ۲۰۱۸

Web-based device fingerprinting is the process of collecting security information through the browser to perform stateless device identification. Fingerprints may then be used to identify and track computing devices in the web. There are various reasons why device-related information may be needed. Among the others, this technique could help to efficiently analyze security information for sustainability. In this paper we introduce a fingerprinting analytics tool that discovers the most appropriate device fingerprints and their corresponding optimal implementations. The fingerprints selected in the result of the performed analysis are used to enrich and improve an open-source fingerprinting analytics tool Fingerprintjs2, daily consumed by hundreds of websites. As a result, the paper provides a noticeable progress in analytics of dozens of values of device fingerprints, and enhances analysis of fingerprints security information.

Introduction

In the recent years, Internet has become an essential part of everyday social and business life for billions of people around the world. Internet users exploit on a daily-basis a vast range of web-based applications, ranging from on-line 5 shopping and banking to social networks. As more and more on-line business models are based on the necessity of distinguishing one web visitor from another, various authentication approaches are applied [1, 2]. In addition to authenticate users and provide their secure access to web applications [3], also the ability to track them becomes essential. 10 The mechanism which has been so far heavily consumed for this purpose are HTTP cookies [4, 5, 6]. Once a web page is requested, a cookie containing a unique identifier is stored on the user’s computer. Such a practice is fundamental for many websites to ensure a high level of usability. Yet, this mechanism has been recently under high public attention. Due to the continuous rise of 15 privacy awareness in society, many people tend to either block or regularly remove cookies from their computers. Moreover, forthcoming laws and directives restrict the future usage of this storage type. The past decade, however, showed that there are other mechanisms besides cookies that enable authentication and tracking web users. In [7], the authors 20 proposed to combine computing device (e.g. desktop computer, smartphone, laptop or tablet) attributes in order to create, with a high likelihood, a unique device-specific identifier, called also a fingerprint. Fingerprinting is possible, because nowadays it is very unlikely that a set of random users, their devices, installed software or its settings will not differ in any way [8, 9, 10]. Information 25 such as User-Agent header, screen resolution, hardware fingerprint (e.g. audio, canvas) or approximate location based on the IP address are just a few of the reasons for devices to differ. Despite above mentioned attributes are individually non-identified, once combined together, they hold an invaluable identification properties, which allow to uniquely discover the type of device and associate it 30 with its user. The simplest solution to get the final user identifier (out of the attributes vector) is to apply a hash function to all of the information concatenated into one string. If none of fingerprint attributes changes over different visits of the user, such hash does not differ between consecutive executions of the algorithm, and therefore, it can be used to identify and re-identify a user 35 visiting a web page. Unfortunately, the device attributes that are used to generate fingerprints may often be altered, for example by a daily software updates or by modified personalization settings.