DOCSLIB.ORG

Threatmetrix Whitepaper

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Threatmetrix Whitepaper Smart Device Identification for Cloud-Based Fraud Prevention Alisdair Faulkner Chief Products Officer White Paper: Smart Device Identification for Cloud-Based Fraud Prevention Contents Basic Device Identification is no longer enough ........ 3 Times have changed but your Device ID hasn’t .......................................................................... 3 Cookies are Obsolete .................................................................................................................. 5 Device Fingerprints Smudge and Fraudsters Wear Gloves ........................................................ 6 Compromised Devices are Commodities .................................................................................... 7 Smart Device Identification Requirements .................................................................................. 8 Smart versus Basic Device Identification Comparison ................................................................ 9 ThreatMetrix Smart Device Identification ................. 11 Identify Fraudsters and Authenticate Customers ...................................................................... 11 Cookieless Device Fingerprinting .............................................................................................. 12 IP, Browser and Packet Fingerprint Interrogation ..................................................................... 13 Real-time complex attribute matching and confidence scoring ................................................. 15 Man-In-The-Middle/Hidden Proxy and True Origin detection .................................................... 17 Compromised Device and Script detection ............................................................................... 18 Integrated Contextual Risk Scoring and Decisioning ................................................................ 19 Recommendations ................................................... 22 Page 2 White Paper: Smart Device Identification for Cloud-Based Fraud Prevention Basic Device Identification is no longer enough Times have changed but your Device ID hasn’t Device Identification, using a visitor’s computer to provide additional fraud prevention and authentication intelligence, remains the most effective first perimeter of defense to protect online transactions including payments, logins and registrations. Benefits include: • Zero customer imposition, providing passive two factor authentication for online transactions without requiring software or hardware tokens or challenge questions. • Not relying on the collection of personal identifying information (PII) • Stops first-time fraud attempts based on device anomalies and global behavior. Unfortunately since first generation device identification technologies were introduced the world has changed dramatically with an increase in the sophistication and globalization of cybercrime and a corresponding increase in exposure to enterprise fraud, risk and security teams. In this whitepaper you will learn about reasons to upgrade basic device identification and fingerprinting methods including: • The reliance of existing technologies on cookie or cookie equivalents. Browser and flash cookies are easy to delete and compromise. Private browsing modes make it easier for fraudsters to hide. Modern smartphones are harder to reliably tag. • Important security data is being ignored when collecting the device fingerprint. Simple browser fingerprinting technologies only gather information about the browser which is easy to spoof or subvert and it ignores important information encoded in the connection and packet. • Relying on simple hashing techniques to perform fingerprint matching misses fraud and causes false positives. Traditional SQL databases cannot perform the complex and extensive attribute matching needed in real time. • Lack of sophisticated proxy and Man-In-The-Middle detection. Simple IP proxy lists are no longer effective. • No knowledge of when a good customer’s device has been compromised. The widespread problem of infected computers due to botnets and Trojans means that simply recognizing an authenticated device is insufficient if that computer is now controlled or spied upon by hackers. Page 3 White Paper: Smart Device Identification for Cloud-Based Fraud Prevention In addition, you will learn new features and benefits associated with the next generation of ThreatMetrix smart device identification technologies including: • Cookieless device fingerprinting for better return visitor recognition • Multiple scoring techniques to truly validate the identity of a device • Going beyond simple browser fingerprinting technology to prevent more fraud • Real-time complex device fingerprint matching and confidence scoring for less false positives • Automatic detection of hidden proxies, compromised devices and MITM attacks to stop cybercrime at time of transaction. • Global device recognition and behavior tracking for proactive protection • Context aware risk based assessment across customer and transaction authentication processes for greater enterprise control. Page 4 White Paper: Smart Device Identification for Cloud-Based Fraud Prevention Cookies are Obsolete 2010 officially rang in the death knell for cookies as a way to reliably identify a device to prevent fraud underscored by Gartner analyst Aviva Litan in her report published in February of 2010 titled “ Privacy Collides With Fraud Detection and Crumbles Flash Cookies”. While it might seem obvious that a fraudster would delete browser cookies to avoid being identified the issue is slightly more nuanced. First generation device identification technologies rely on the general public’s and unsophisticated fraudster’s ignorance of Flash Cookies (Local Storage Objects) that are not deleted when regular browser cookies are cleared, and are invisible unless you know where to find them. Unfortunately for Basic Device identification vendors, online advertisers also use these same LSOs to resuscitate a cleared cookie which in turn, has incited a furor with privacy advocates. The result has attracted the attention of the FTC and the US Congress to impose privacy regulations to protect consumer’s rights. In response the browser and browser plugin companies have instituted private browsing and opt out features into their products to better accommodate consumer opt-out protection. Additionally, version 10.1 of Adobe’s Flash product now enables browser companies and consumers to delete LSOs in line with regular cookies. In addition, all the major browser companies have now implemented some form of private browsing mode that allows customers and intrepid fraudsters to temporarily suppress cookies and Flash objects and hence evade re-identification. 2010 also saw an explosive uptake in the number and variety of tablets and touch-based smartphones that make accessing the Internet and performing an online transaction from a mobile device a practical reality. Some of these devices such as the iPhone and iPad do not Page 5 White Paper: Smart Device Identification for Cloud-Based Fraud Prevention support Flash and also block third-party browse cookies by default further reducing the effectiveness of cookies and first generation device identification solutions for device recognition and reputation. Device Fingerprints Smudge and Fraudsters Wear Gloves Every interaction a customer makes with a website leaves a digital fingerprint about the device, the type of browser and the connection used. First generation device fingerprinting technologies typically use JavaScript or Flash to collect browser and clock information and use a hashing algorithm to generate some form of identifier. The problem is that this device fingerprint routinely changes as customers swap browsers, change physical locations and corresponding IP addresses with laptops, tablets and smartphones. As an illustration, a sample of transactions from ThreatMetrix Fraud Network shows that after 2 months 20% of visitors had changed their browser, and 25% had multiple IP Addresses. Further, fraudsters will deliberately try to manipulate or block browser settings in order to disguise their device fingerprint. The following graphs from the same sample shows that nearly 10% of transactions had one or more of JavaScript, Flash or cookies suppressed. Some of these transactions are fraudulent while at the same time many are transactions executed by privacy conscious customers and are valid. If these devices are not properly identified the end result to an ecommerce merchant, financial institution or other business will be either an increase of false positives resulting in loss revenues or increases in fraud resulting in increased costs. Page 6 White Paper: Smart Device Identification for Cloud-Based Fraud Prevention Compromised Devices are Commodities Thanks to sophisticated malware like Zeus, millions of good customer’s computers go bad on a daily basis. The problem is that existing fraud prevention and security solutions are blind to evidence that a particular device is infected at the point of a transaction leaving the enterprise exposed to Man-In-The-Browser (MITB), key-logging and Man-In-The-Middle (MITM) attacks. By orders of magnitude, however, the most common use of compromised computers is to turn an innocent’s computer into an IP proxy to avoid geolocation filters and known anonymous proxy IP lists. Using a real world example, one ThreatMetrix customer doing an average of 4,500 customer verification transactions a day had nearly 5% of transaction
Load more

Recommended publications
  • Vmlogin Virtual Multi-Login Browser Instruction Ver
    VMLogin virtual multi-login browser Instruction Ver 1.0 Skype: [email protected] Telegram1: Vmlogin Telegram2: vmlogin_us System Requirements Hardware requirements RAM: 4GB recommended; 1GB available disk space Effective GPU Supported operating systems Although VMLogin supports x86 (32-bit) systems, we recommend using VMLogin on x64 (64-bit) systems. OS supported by VMLogin Windows 10 Windows Server 2016 Windows 7 Windows 8 Software install Users can download the latest software installation package from the official website: vmlogin.us. The version used in this manual is 1.0.3.8. We can start the software installation package program, and the interface for selecting the installation language will be displayed: User can select the software language he likes, and can also change the language in the settings after installation. Currently supports Simplified Chinese and English. After the user select the language, enter the interface for selecting the installation directory: Here is generally installed by default, press the "Next" button, if the user customizes the installation directory, please pay attention to the directory permissions, the current user can edit. Generally, you need to create a desktop shortcut for convenient, we click the "Next" button to continue by default here. At this point we can click the "Install" button to install. We need to wait for the installation progress to complete. At this step, the software installation is complete. Software function 1. Start Software When we start the software, we will see the login interface. New users need to create a new account to log in. 2. Create Account To register as a new user, you need to use email as your account, set a password, and register.
    [Show full text]
  • Device Fingerprinting for Augmenting Web Authentication: Classification and Analysis of Methods
    Device Fingerprinting for Augmenting Web Authentication: Classification and Analysis of Methods Furkan Alaca P.C. van Oorschot School of Computer Science Carleton University, Ottawa, Canada ABSTRACT ing habits [21,2]. In addition, a number of commercial Device fingerprinting is commonly used for tracking users. fraud detection services [38, 57] employ browser-based de- We explore device fingerprinting but in the specific context vice fingerprinting to identify fraudulent transactions. Large of use for augmenting authentication, providing a state-of- web services can mitigate insecure passwords in part by the-art view and analysis. We summarize and classify 29 using server-side intelligence in the authentication process available methods and their properties; define attack models with \multidimensional" authentication [14], wherein con- relevant to augmenting passwords for user authentication; ventional passwords are augmented by multiple implicit sig- and qualitatively compare them based on stability, repeata- nals collected from the user's device without requiring user bility, resource use, client passiveness, difficulty of spoofing, intervention. We explore the applicability of various finger- and distinguishability offered. printing techniques to strengthening web authentication and evaluate them based on the security benefits they provide. CCS Concepts We aim to identify techniques that can improve security, while being ideally invisible to users and compatible with •Security and privacy ! Authentication; Web appli- existing web browsers, thereby imposing zero cost on us- cation security; ability and deployability. We identify and classify 29 available device fingerprint- Keywords ing mechanisms, primarily browser-based and known, but Device fingerprinting, multi-dimensional authentication, pass- including several network-based methods and others not in words, comparative analysis, comparative criteria the literature; identify and assess their properties suitable for application to augment user authentication; define a se- 1.
    [Show full text]
  • Web-Based Fingerprinting Techniques
    Web-based Fingerprinting Techniques V´ıtor Bernardo and Dulce Domingos LaSIGE, Faculdade de Ciencias,ˆ Universidade de Lisboa, Lisboa, Portugal Keywords: Browser Fingerprinting, Cross-browser Fingerprinting, Device Fingerprinting, Privacy, Fingerprint. Abstract: The concept of device fingerprinting is based in the assumption that each electronic device holds a unique set of physical and/or logical features that others can capture and use to differentiate it from the whole. Web-based fingerprinting, a particular case of device fingerprinting, allows website owners to differentiate devices based on the set of information that browsers transmit. Depending on the techniques being used, a website can track a device based on its browser features (browser fingerprinting) or based on system settings (cross-browser fingerprinting). The latter allows identification of the device even when more than one browser is used. Several different works have introduced new techniques over the last years proving that fingerprinting can be done in multiple ways, but there is not a consolidated work gathering all of them. The current work identifies known web-based fingerprinting techniques, categorizing them as which ones are browser and which are cross-browser and showing real examples of the data that can be captured with each technique. The study is synthesized in a taxonomy, which provides a clear separation between techniques, making it easier to identify the threats to security and privacy inherent to each one. 1 INTRODUCTION far more upsetting than simple cookies. In most cases, web-based fingerprinting is used to Device fingerprinting is based on the assumption that track users activity in sites and bind a device finger- no two devices are exactly alike and that profiles can print to a user profile (together with its preferences, be created by capturing the emanation patterns sent tastes and interests).
    [Show full text]
  • The Elephant in the Background: a Quantitative Approach to Empower Users Against Web Browser Fingerprinting
    The Elephant in the Background: A Quantitative Approach to Empower Users Against Web Browser Fingerprinting Anonymous Abstract—Tracking users is a ubiquitous practice in the web Furthermore, other researchers have concentrated their ef- today. User activity is recorded on a large scale and analyzed forts on creating new fingerprinting techniques, e.g. by using by various actors to create personalized products, forecast future canvas [24] and audio objects [30] or via CSS [37]. Others behavior, and prevent online fraud. While so far HTTP cookies have significantly improved known techniques with machine have been the weapon of choice, new and more pervasive learning [42] or by extracting fingerprinting features more techniques such as browser fingerprinting are gaining traction. passively via extension activity tracking [36]. Hence, in this paper, we describe how users can be empowered against JavaScript fingerprinting by showing them when, how, The sheer diversity of fingerprinting techniques available and who is tracking them. To this end, we conduct a systematic analysis of various JavaScript fingerprinting tools. Based on this to us begs the question of which techniques are really being analysis, we design and develop FPMON: a light-weight and used and which ones are not? So far, large scale studies have comprehensive fingerprinting monitor that measures and rates only uncovered specific instances of fingerprinting and these JavaScript fingerprinting activity on any given website in real- are over 5-10 years old [1,2]. In particular, we currently lack time. Using FPMON, we evaluate the Alexa 10k most popular a comprehensive view of fingerprinting activity on the web websites to i) study the pervasiveness of JavaScript fingerprinting; today.
    [Show full text]
  • Browser Extension and Login-Leak Experiment
    BROWSER EXTENSION AND LOGIN-LEAK EXPERIMENT IPEN 2017, Vienna Joint work with Nataliia Bielova, Claude Castelluccia Gábor György Gulyás Privatics Team, INRIA http://gulyas.info // @GulyasGG USER TRACKING ON THE WEB 17-06-09 © Gábor György Gulyás 2 The „de-facto” busniess model of the web Advertiser ID=967 User Apples on sale! ID=967 cnn.com 17-06-09 © Gábor György Gulyás 3 Storing the identifier on the client side • Cookies • E-tags – Flash • Last-mod timestamps – HTML5 • HTTP authentication • Caching in files of • HTTP 301 redirect – JavaScript • HSTS caches – CSS – Images (pixel-level) … 17-06-09 © Gábor György Gulyás 4 Browser fingerprinting appears (2010-2012) [3] http://panopticlick.eff.org https://fingerprint.pet-portal.eu • Browser fingerprint • Cross-browser fingp. – Flash/Java required – Device fingerprint (for 95% uniqueness) – No plugins, just JS – Browser dependent – Concept appears later in the wild 17-06-09 © Gábor György Gulyás 5 Fingerprinting penetration (2013-2016) 2013: Alexa TOP 10k. • 20 pages deep • 0,4% adoption (40 sites) • Skype.com, porn and dating • 3 804 less popular sites are tracked Nickiforakis et al.: Cookieless monster: Exploring the ecosystem of web-based device fingerprinting (2013) 2016: Alexa TOP 1M. S. Englehardt, A. Narayanan: Online tracking: A 1-illion-site measurement and analysis (2016) 17-06-09 © Gábor György Gulyás 6 Behavioral fingerprinting You are what you install to you computer? Fonts are good indicators of what is installed. Boda et al.: User Tracking on the Web via Cross-Browser Fingerprinting (2011) Google.com Youtube.com Facebook.com Baidu.com The list of the sites you have Yahoo.com Wikipedia.org visited also describe you well.
    [Show full text]
  • The Elephant in the Background: a Quantitative Approach to Empower Users Against Web Browser Fingerprinting
    EasyChair Preprint № 4473 The Elephant in the Background: A Quantitative Approach to Empower Users Against Web Browser Fingerprinting Julian Fietkau, Kashjap Thimmaraju, Felix Kybranz, Sebastian Neef and Jean-Pierre Seifert EasyChair preprints are intended for rapid dissemination of research results and are integrated with the rest of EasyChair. October 26, 2020 The Elephant in the Background: A Quantitative Approach to Empower Users Against Web Browser Fingerprinting Julian Fietkau, Kashyap Thimmaraju∗, Felix Kybranz, Sebastian Neef, Jean-Pierre Seifert Technische Universitat¨ Berlin, Humboldt-Universitat¨ Berlin∗ ffietkau, f.kybranz, [email protected], fkash, [email protected] Abstract—Tracking users is a ubiquitous practice in the web Furthermore, other researchers have concentrated their ef- today. User activity is recorded on a large scale and analyzed forts on creating new fingerprinting techniques, e.g. by using by various actors to create personalized products, forecast future canvas [24] and audio objects [30] or via CSS [37]. Others behavior, and prevent online fraud. While so far HTTP cookies have significantly improved known techniques with machine have been the weapon of choice, new and more pervasive learning [42] or by extracting fingerprinting features more techniques such as browser fingerprinting are gaining traction. passively via extension activity tracking [36]. Hence, in this paper, we describe how users can be empowered against JavaScript fingerprinting by showing them when, how, The sheer diversity of fingerprinting
    [Show full text]
  • Future of Device Fingerprinting
    WHITE PAPER Future of Device Fingerprinting Contents Introduction 1 Limitations of Traditional Device Fingerprinting 2 Device Fingerprinting Evolution 3 How Simility’s Device Recon Works 4 Individual Use Cases 7 Today’s device fingerprinting technology has been made significantly more effective at fighting online fraud with advanced techniques such as fuzzy matching, clustering and predictive modeling. Introduction to Device Fingerprinting For over a decade, a crucial part of fraud detection in the virtual world has been assigning an identity to every laptop, tablet and mobile device that accesses a website or app. Such a fingerprint, often referred as a device fingerprint or device ID, is a representation of hundreds of different device-specific values taken from an end user’s device. Like in the real world, a device fingerprint aids in identification and tracking of bad actors. As a first step to any fraud attempt, fraudsters try to hide their identity or pretend to be many different people when they are, in fact, just one person. For example, a person who owns a local barber shop may want to publish a lot of fake positive reviews about his business and negative reviews about the competitors’ businesses on a review app. Obviously, the person will use a fake name because his customers and competitors know about his business. Also, the business review site probably requires a different email address for each account, so he might enter fake email addresses when setting up multiple fake accounts. Perhaps the review app even requires each account to be from a different IP address and to register a new phone number.
    [Show full text]
  • Browser Fingerprint Coding Methods Increasing the Effectiveness of User Identification in the Web Traffic
    JAISCR, 2020, Vol. 10, No. 4, pp. 243 – 253 10.2478/jaiscr-2020-0016 BROWSER FINGERPRINT CODING METHODS INCREASING THE EFFECTIVENESS OF USER IDENTIFICATION IN THE WEB TRAFFIC 1, 2 3 Marcin Gabryel ∗, Konrad Grzanek , Yoichi Hayashi 1Department of Computer Engineering, Czestochowa University of Technology, al. Armii Krajowej 36, 42-200 Cze¸stochowa, Poland 2Information Technology Institute, University of Social Sciences, 90 - 113 Lodz Clark University, Worcester, MA 01610, USA 3Department of Computer Science, Meiji University, Japan ∗E-mail: [email protected] Submitted: 14th October 2019; Accepted: 29th April 2020 Abstract Web-based browser fingerprint (or device fingerprint) is a tool used to identify and track user activity in web traffic. It is also used to identify computers that are abusing online advertising and also to prevent credit card fraud. A device fingerprint is created by extract- ing multiple parameter values from a browser API (e.g. operating system type or browser version). The acquired parameter values are then used to create a hash using the hash func- tion. The disadvantage of using this method is too high susceptibility to small, normally occurring changes (e.g. when changing the browser version number or screen resolution). Minor changes in the input values generate a completely different fingerprint hash, mak- ing it impossible to find similar ones in the database. On the other hand, omitting these unstable values when creating a hash, significantly limits the ability of the fingerprint to distinguish between devices. This weak point is commonly exploited by fraudsters who knowingly evade this form of protection by deliberately changing the value of device pa- rameters.
    [Show full text]
  • A Systematic Framework for Categorising Iot Device Fingerprinting Mechanisms
    This is a repository copy of Position paper : A systematic framework for categorising IoT device fingerprinting mechanisms. White Rose Research Online URL for this paper: https://eprints.whiterose.ac.uk/169799/ Version: Published Version Proceedings Paper: Yadav, Poonam, Feraudo, Angelo, Arief, Budi et al. (2 more authors) (2020) Position paper : A systematic framework for categorising IoT device fingerprinting mechanisms. In: AIChallengeIoT '20: Proceedings of the 2nd International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things. Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things, 16 Nov 2020 ACM AIChallengeIoT (Sensys 2020) . Association for Computing Machinery (ACM) , 62–68. https://doi.org/10.1145/3417313.3429384 Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ Position paper: A systematic framework for categorising IoT device fingerprinting mechanisms Poonam Yadav Angelo Feraudo Budi Arief University of York, UK University of York, UK University of Kent, UK Siamak F.
    [Show full text]
  • Mobile Device Fingerprinting and Unique User Identification Media and Entertainment (M&E)
    White Paper Mobile Device Fingerprinting and Unique User Identification Media and Entertainment (M&E) Table of Contents Executive Summary 01 Introduction 02 Solution Overview 02 Scenario 1: User identification and tracking for mobile web (cookie-less approach) 03 Scenario 2: User identification and tracking with SDK 05 Conclusion 07 Executive Summary The principles of user or device tracking in the online world and the mobile world are basically similar. Identifying and tracking user profiles without Personally Identifiable Information (PII) is something being undertaken by a number of the entities in the delivery of targeted mobile ad campaigns. A device fingerprint is a set of information collected about a device or user profile for the purpose of audience-based content or ad targeting. Although the cookies are turned off, it is still feasible to use fingerprints for complete or partial identification of individual users or devices. Device fingerprinting, sometimes known as digital fingerprinting, claims that it can assist businesses to enhance online or mobile advertising and diminish fraud risk. 01 Introduction The purpose of this whitepaper is to outline various scenarios and implementation approaches for mobile device fingerprinting, unique user identification, and user tracking to: • Get insights into mobile campaign performance events such as clicks and installs • Track which app was installed and which user installed the app • Track users’ behavior and browsing patterns to enhance your content or ad recommendation engine Solution Overview The challenge The key challenge is to uniquely identify a mobile user. Mobile users access content through both native applications and mobile web. On mobile devices, websites and applications are regarded as disparate domains.
    [Show full text]
  • Iot Device Fingerprint Using Deep Learning
    Accepted for publication in 2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS) Please refer DOI: 10.1109/IOTAIS.2018.8600824 for final manuscript IoT Device Fingerprint using Deep Learning Sandhya Aneja Nagender Aneja Md Shohidul Islam Faculty of Integrated Technologies Institute of Applied Data Analytics Faculty of Integrated Technologies Universiti Brunei Darussalam Universiti Brunei Darussalam Universiti Brunei Darussalam Brunei Darussalam Brunei Darussalam Brunei Darussalam [email protected] [email protected] [email protected] Abstract—Device Fingerprinting (DFP) is the identification extract IAT that is the time interval between consecutively of a device without using its network or other assigned received packets. In this paper, we worked on the DFP identities including IP address, Medium Access Control using IAT to create IAT fingerprint using deep learning. (MAC) address, or International Mobile Equipment Identity (IMEI) number. DFP identifies a device using information IAT is unique for each device due to the different hardware from the packets which the device uses to communicate over and software used for the device and thus is more useful in the network. Packets are received at a router and processed the wireless networks and Internet of Things (IoT) devices to extract the information. In this paper, we worked on the where communication is highly vulnerable due to identity DFP using Inter Arrival Time (IAT). IAT is the time interval issues. between the two consecutive packets received. This has been observed that the IAT is unique for a device because of This work presents a novel idea of DFP by plotting the different hardware and the software used for the device.
    [Show full text]
  • DEVICE and BROWSER FINGERPRINTING AS SENSITIVE PERSONAL DATA Mark Nottingham
    NOT SIMILAR TO COOKIES: DEVICE AND BROWSER FINGERPRINTING AS SENSITIVE PERSONAL DATA Mark Nottingham I Introduction Browser and device fingerprinting are especially pernicious methods of online tracking, but most legal responses have not distinguished them from a more well- known mechanism, cookies, despite having a reputation as being ‘creepy’.1 I contend that they deserve separate consideration, owing to the distinct privacy challenges they present. In this paper I briefly explain what fingerprinting is, summarise how online trackers use it, and explore the privacy issues raised, especially as compared to cookies. I evaluate the current constraints on fingerprinting using Lessig’s norms/ market/architecture/law framework of regulation modalities. I then discuss why fingerprints should not only be considered as distinct from cookies, but should also be considered as sensitive data, deserving of a distinct legal response. Finally, I outline potential legal responses to fingerprinting, exploring their tradeoffs. II What is fingerprinting? In this paper, fingerprinting refers to two related techniques — browser fingerprinting and device fingerprinting. Browser fingerprinting is ‘a method of web browser identification based on information provided by each web browser, such as the screen size, the list of installed plugins, system languages, and other [mechanisms].’2 Device fingerprinting is the use of similar methods to identify the device being used — e.g., the desktop, laptop, or mobile computer. This technique can be successful even when different software is used to access a service (e.g., after switching Web browsers).3 1 See, eg, Adam Tanner, ‘The Web Cookie Is Dying. Here’s The Creepier Technology That Comes Next’, Forbes (online, 17 June 2013) <https://www.forbes.com/sites/adamtanner/2013/06/17/the-web-cookie-is- dying-heres-the-creepier-technology-that-comes-next/>.
    [Show full text]