Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Proceedings on Privacy Enhancing Technologies

4 Issues per year

Open Access
See all formats and pricing
More options …

Near-Optimal Fingerprinting with Constraints

Gábor György Gulyás / Gergely Acs / Claude Castelluccia
Published Online: 2016-07-14 | DOI: https://doi.org/10.1515/popets-2016-0051


Several recent studies have demonstrated that people show large behavioural uniqueness. This has serious privacy implications as most individuals become increasingly re-identifiable in large datasets or can be tracked, while they are browsing the web, using only a couple of their attributes, called as their fingerprints. Often, the success of these attacks depends on explicit constraints on the number of attributes learnable about individuals, i.e., the size of their fingerprints. These constraints can be budget as well as technical constraints imposed by the data holder. For instance, Apple restricts the number of applications that can be called by another application on iOS in order to mitigate the potential privacy threats of leaking the list of installed applications on a device. In this work, we address the problem of identifying the attributes (e.g., smartphone applications) that can serve as a fingerprint of users given constraints on the size of the fingerprint. We give the best fingerprinting algorithms in general, and evaluate their effectiveness on several real-world datasets. Our results show that current privacy guards limiting the number of attributes that can be queried about individuals is insufficient to mitigate their potential privacy risks in many practical cases.


  • [1] European Commission. Proposal for European Parliament and the Council (General Data Protection Regulation), 2012. http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2012:0011:FIN:EN:PDF.Google Scholar

  • [2] Apple uikit framework reference, entry on canopenurl call. https://developer.apple.com/library/ios/documentation/UIKit/Reference/UIApplication_Class/#//apple_ref/occ/instm/UIApplication/canOpenURL:, 2016.Google Scholar

  • [3] G. Acar, C. Eubank, S. Englehardt, M. Juarez, A. Narayanan, and C. Diaz. The web never forgets: Persistent tracking mechanisms in the wild. In ACM CCS, pages 674-689, 2014.Google Scholar

  • [4] J. P. Achara, G. Acs, and C. Castelluccia. On the unicity of smartphone applications. In Proceedings of WPES, pages 27-36. ACM, 2015.Google Scholar

  • [5] Article 29 Data Protection Working Party. Opinion 05/2014 on anonymization techniques, April 2014.Google Scholar

  • [6] K. Boda, A. M. Földes, G. G. Gulyás, and S. Imre. User tracking on the web via cross-browser fingerprinting. In P. Laud, editor, Information Security Technology for Applications, volume 7161 of LNCS, pages 31-46. 2012.Google Scholar

  • [7] T. Bujlow, V. Carela-Espanol, J. Sole-Pareta, and P. Barlet- Ros. Web tracking: Mechanisms, implications, and defenses. In http://arxiv.org/abs/1507.07872, 2015.Google Scholar

  • [8] X. Cai, X. C. Zhang, B. Joshi, and R. Johnson. Touching from a distance: Website fingerprinting attacks and defenses. In ACM CCS, 2012.Google Scholar

  • [9] C. Chekuri and A. Kumar. Maximum coverage problem with group budget constraints and applications. In Approximation, Randomization, and Combinatorial Optimization, pages 72-83. Springer LNCS, 2004.Google Scholar

  • [10] Y.-A. de Montjoye, C. A. Hidalgo, M. Verleysen, and V. D. Blondel. Unique in the crowd: The privacy bounds of human mobility. Scientific Reports, Nature, March 2013.Web of ScienceGoogle Scholar

  • [11] Y.-A. de Montjoye, L. Radaelli, V. K. Singh, and A. Pentland. Unique in the shopping mall: On the reidentifiability of credit card metadata. Science, 347(6221), January 2015.Google Scholar

  • [12] P. Eckersley. How unique is your web browser? In PETS, pages 1-18, 2010.Google Scholar

  • [13] U. Feige. A threshold of ln(n) for approximating set cover. Journal of the ACM, 45(4):634-652, July 1998.Google Scholar

  • [14] gacar. Improve persistence and webfont compatibility of font patch, comment #13. https://trac.torproject.org/projects/tor/ticket/5798comment:#13, 2013.Google Scholar

  • [15] M. Graham and S. D. Sabbata. The anonymous internet. http://geography.oii.ox.ac.uk/?page=tor, 2014.Google Scholar

  • [16] G. G. Gulyás, G. Acs, and C. Castelluccia. Update your tor browser settings - otherwise it is less anonymous than you would think. https://gulyas.info/blog/read/16/2015-11-30-Update-your-TOR-Browser-settings-otherwise-it-is-lessanonymous-than-you-would-think.php, 11 2015.Google Scholar

  • [17] J. Hayes and G. Danezis. k-fingerprinting: a robust scalable website fingerprinting technique. In http://arxiv.org/abs/1509.00789, 2015.Google Scholar

  • [18] D. Lemire, L. Boytsov, and N. Kurz. SIMD compression and the intersection of sorted integers. CoRR, abs/1401.6399, 2014.Web of ScienceGoogle Scholar

  • [19] J. Marshall. Twitter is tracking users’ installed apps for ad targeting. http://blogs.wsj.com/cmo/2014/11/26/twitteris-tracking-users-installed-apps-for-ad-targeting/, 11 2014.Google Scholar

  • [20] E. C. Mike Perry and S. Murdoch. The design and implementation of the tor browser [draft]. https://www.torproject.org/projects/torbrowser/design/, 5 2015.Google Scholar

  • [21] R. Motwani and Y. Xu. Efficient algorithms for masking and finding quasi-identifiers. In VLDB, 2007.Google Scholar

  • [22] G. Nemhauser, L. Wolsey, and M. Fisher. An analysis of approximations for maximizing submodular set functions i. Mathematical Programming, 14(1):265-294, 1978.Google Scholar

  • [23] N. Nikiforakis, A. Kapravelos, W. Joosen, C. Kruegel, F. Piessens, and G. Vigna. Cookieless monster: Exploring the ecosystem of web-based device fingerprinting. In IEEE Symposium on S&P, pages 541-555, May 2013.Google Scholar

  • [24] L. Olejnik, C. Castelluccia, and A. Janc. On the uniqueness of web browsing history patterns. Annals of Telecommunications, 69(1), February 2014.Web of ScienceGoogle Scholar

  • [25] A. J. Oliner, A. P. Iyer, I. Stoica, E. Lagerspetz, and S. Tarkoma. Carat: Collaborative energy diagnosis for mobile devices. In ACM SenSys, 2013.Google Scholar

  • [26] H. T. T. Truong, E. Lagerspetz, P. Nurmi, A. J. Oliner, S. Tarkoma, N. Asokan, and S. Bhattacharya. The company you keep: Mobile malware infection rates and inexpensive risk indicators. In WWW, 2014.Google Scholar

  • [27] H. Zang and J. Bolot. Anonymization of location data does not work: A large-scale measurement study. In MobiCom, 2011.Google Scholar

About the article

Received: 2016-02-29

Revised: 2016-06-02

Accepted: 2016-06-02

Published Online: 2016-07-14

Published in Print: 2016-10-01

Citation Information: Proceedings on Privacy Enhancing Technologies, Volume 2016, Issue 4, Pages 470–487, ISSN (Online) 2299-0984, DOI: https://doi.org/10.1515/popets-2016-0051.

Export Citation

© 2016. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

Comments (0)

Please log in or register to comment.
Log in