Keystroke Dynamics Anonymization System

Keystroke Dynamics enables the authentication or identification of users by analyzing their way of typing, e.g. when browsing the Internet. Most studies in the state of the art focus on increasing Keystroke Dynamics Sys- tems performances. In this paper, we address the problematic of avoiding the biometric capture of keystroke dynamics in order to protect users’ privacy. Authentication/identification, profiling can be considered as at- tacks we limit in this contribution. Experimental results obtained on significant datasets show the benefits of the proposed approaches.

[1]  Zachary Weinberg,et al.  I Still Know What You Visited Last Summer: Leaking Browsing History via User Interaction and Side Channel Attacks , 2011, 2011 IEEE Symposium on Security and Privacy.

[2]  Fabian Monrose,et al.  Keystroke dynamics as a biometric for authentication , 2000, Future Gener. Comput. Syst..

[3]  Norman Shapiro,et al.  Authentication by Keystroke Timing: Some Preliminary Results , 1980 .

[4]  Christophe Rosenberger,et al.  A new soft biometric approach for keystroke dynamics based on gender recognition , 2012, Int. J. Inf. Technol. Manag..

[5]  Frank Piessens,et al.  FPDetective: dusting the web for fingerprinters , 2013, CCS.

[6]  Wouter Joosen,et al.  PriVaricator: Deceiving Fingerprinters with Little White Lies , 2015, WWW.

[7]  Clayton Charles Epp,et al.  Identifying emotional states through keystroke dynamics , 2010 .

[8]  Christophe Rosenberger,et al.  Web-Based Benchmark for Keystroke Dynamics Biometric Systems: A Statistical Analysis , 2012, 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[9]  David Umphress,et al.  Identity Verification Through Keyboard Characteristics , 1985, Int. J. Man Mach. Stud..

[10]  Kenneth Revett,et al.  On the Use of Rough Sets for User Authentication Via Keystroke Dynamics , 2007, EPIA Workshops.

[11]  Hyoungjoo Lee,et al.  Retraining a keystroke dynamics-based authenticator with impostor patterns , 2007, Comput. Secur..

[12]  Baptiste Hemery,et al.  Unconstrained keystroke dynamics authentication with shared secret , 2011, Comput. Secur..

[13]  Kenneth Revett,et al.  A machine learning approach to keystroke dynamics based user authentication , 2007, Int. J. Electron. Secur. Digit. Forensics.

[14]  Roy A. Maxion,et al.  The Effect of Clock Resolution on Keystroke Dynamics , 2008, RAID.

[15]  Ting Yu,et al.  On mouse dynamics as a behavioral biometric for authentication , 2011, ASIACCS '11.

[16]  Youtian Du,et al.  User Authentication Through Mouse Dynamics , 2013, IEEE Transactions on Information Forensics and Security.

[17]  Peter Eckersley,et al.  How Unique Is Your Web Browser? , 2010, Privacy Enhancing Technologies.

[18]  Christophe Rosenberger,et al.  GREYC keystroke: A benchmark for keystroke dynamics biometric systems , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.

[19]  Roy A. Maxion,et al.  Comparing anomaly-detection algorithms for keystroke dynamics , 2009, 2009 IEEE/IFIP International Conference on Dependable Systems & Networks.

[20]  Song Li,et al.  (Cross-)Browser Fingerprinting via OS and Hardware Level Features , 2017, NDSS.

[21]  Sándor Imre,et al.  User Tracking on the Web via Cross-Browser Fingerprinting , 2011, NordSec.

[22]  Walter Rudametkin,et al.  Beauty and the Beast: Diverting Modern Web Browsers to Build Unique Browser Fingerprints , 2016, 2016 IEEE Symposium on Security and Privacy (SP).

[23]  Pilsung Kang,et al.  Keystroke dynamics-based user authentication using freely typed text based on user-adaptive feature extraction and novelty detection , 2018, Appl. Soft Comput..

[24]  Jean-Yves Ramel,et al.  User Classification for Keystroke Dynamics Authentication , 2007, ICB.