Journal Title : International Journal of Modern Trends in Engineering and Science


Author’s Name : V Tamilarasan | J Keerthikaunnamed

Volume 03 Issue 12 2016

ISSN no:  2348-3121

Page no: 108-109

Abstract – Each time a user withdraws money from an ATM or unlocks his cell phone, he types the identical four-digit PIN number sequence. Anyone who observes this procedure e.g., by looking over the shoulder of a user, can easily memorize the PIN. In conjunction with stolen or skimmed material such as magnetic stripe cards, account numbers printed on receipts, or mobile devices, criminals easily gain access e.g., to a victimized user’s bank account or telecommuncations services. Requiring users to memorize longer or multiple PIN sequences would have a detrimental effect on recall, and obviously, no substantial improvement will be achieved for as long as they entered information remains constant. Likewise, requiring users to perform complicated mathematical calculations when entering PINs is unreasonable. All this would raise the rate of erroneous PIN entries, which would in turn annoy users and thereby reduce the acceptance of the technology. Moreover, service and operation costs e.g., in the retail banking sector would increase due to a growing number of requests to reset PINs which are commonly blocked after three false entries. The principal idea is to present the user the PIN digits as two distinct sets e.g., by randomly coloring three by forth of the keys green, red and blue respectively. 

Keywords— Authentication; Pin-Entry Method


  1. H. J. Asghar, S. Li, J. Pieprzyk, and H. Wang, “Cryptoanalysis of the convex hull click human identification protocol,” in Proc. 13th Int. Conf.Inf. Secur., 2011, pp. 24–30.
  2. H. J. Asghar, S. Li, R. Steinfeld, and J. Pieprzyk, “Does counting still count? Revisiting the security of counting based user authentication protocols against statistical attacks,” in Proc. 20th Symp. Internet Soc.Netw. Distrib. Syst. Secur. (NDSS), Apr. 2013, pp. 1–18.
  3. X. Bai, W. Gu, S. Chellappan, X. Wang, D. Xuan, and B. Ma, “PAS:Predicate-based authentication services against powerful passive adversaries,”in Proc. IEEE Annu. Comput. Secur. Appl. Conf., Dec. 2008,pp. 433–442.
  4. A. Bianchi, I. Oakley, and D. S. Kwon, “Counting clicks and beeps:Exploring numerosity based haptic and audio PIN entry,” Interact.Comput., vol. 24, no. 5, pp. 409–422, Sep. 2012.
  5. A. De Luca, K. Hertzschuch, and H. Hussmann, “ColorPIN—Securing PIN entry through indirect input,” in Proc. ACM CHI Conf. Human Factors Comput. Syst., 2010, pp. 1103–1106.
  6. A. De Luca, E. von Zezschwitz, and H. Hußmann, “VibraPass-secure authentication based on shared lies,” in Proc. ACM CHI Conf. Human Factors Comput. Syst., 2009, pp. 913–916.
  7. P. Dunphy, A. P. Heiner, and N. Asokan, “A closer look at recognitionbased graphical passwords on mobile devices,” in Proc. 6th Symp.Usable Privacy Secur., 2010, pp. 1–12.
  8. P. Golle and D. Wagner, “Cryptanalysis of a cognitive authentication scheme,” in Proc. IEEE Symp. Secur. Privacy, May 2007, pp. 66–70.
  9. N. J. Hopper and M. Blum, “Secure human identification protocols,”in Advances in Cryptology—ASIACRYPT. Berlin, Germany: Springer-Verlag, 2001, pp. 52–66.
  10. D. Kim et al., “Multi-touch authentication on tabletops,” in Proc. ACM SIGCHI Conf. Human Factors Comput. Syst. (CHI), 2010,pp. 1093–1102.
  11. T. Kwon, S. Shin, and S. Na, “Covert attentional shoulder surfing:Human adversaries are more powerful than expected,” IEEE Trans. Syst.,Man, Cybern., Syst., vol. 44, no. 6, pp. 716–727, Jan. 2014.
  12. S. Li, H. J. Asghar, J. Pieprzyk, A. Sadeghi, R. Schmitz, and H. Wang,“On the security of PAS (predicate-based authentication service),” in Proc. Annu. Comput. Secur. Appl. Conf., Dec. 2009, pp. 209–218.
  13. J. Long, No Tech Hacking: A Guide to Social Engineering, Dumpster Diving, and Shoulder Surfing. Boston, MA, USA: Syngress, 2008.
  14. T. Matsumoto and H. Imai, “Human identification through insecure channel,” in Advances in Cryptology—EUROCRYPT. Berlin, Germany:Springer-Verlag, 1991, pp. 409–421.
  15. Y. Michalevsky, D. Boneh, and G. Nakibly, “Gyrophone: Recognizing speech from gyroscope signals,” in Proc. USENIX Secur. Symp.Aug. 2014, pp. 1053–1067.