Javier Parra-Arnau
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Abstract
The intrusiveness and the increasing invasiveness of online advertising have, in the last few years, raised serious concerns regarding user privacy and Web usability. As a reaction to these concerns, we have witnessed the emergence of a myriad of ad-blocking and antitracking tools, whose aim is to return control to users over advertising. The problem with these technologies, however, is that they are extremely limited and radical in their approach: users can only choose either to block or allow all ads. With around 200 million people regularly using these tools, the economic model of the Web—in which users get content free in return for allowing advertisers to show them ads—is at serious peril. In this article, we propose a smart Web technology that aims at bringing transparency to online advertising, so that users can make an informed and equitable decision regarding ad blocking. The proposed technology is implemented as a Web-browser extension and enables users to exert fine-grained control over advertising, thus providing them with certain guarantees in terms of privacy and browsing experience, while preserving the Internet economic model. Experimental results in a real environment demonstrate the suitability and feasibility of our approach, and provide preliminary findings on behavioral targeting from real user browsing profiles.
- Adblock Plus. Retrieved from https://adblockplus.org.Google Scholar
- Clickstream or clickpath analysis. Retrieved from http://www.opentracker.net/article/clickstream-or-clickpath-analysis. Accessed on 2015-03-27. [Online].Google Scholar
- COIN-OR Interior Point OPTimizer. Retrieved from https://projects.coin-or.org/Ipopt.Google Scholar
- COIN-OR Linear Programming Solver. Retrieved from https://projects.coin-or.org/Clp.Google Scholar
- Consumer Opt-out. Technical Report. Network Advertising Initiative. Retrieved from http://www.networkadvertising.org/choices. Accessed on 2015-03-19.Google Scholar
- Disconnect. Retrieved from https://disconnect.me/.Google Scholar
- Eclipse Public License-Version 1.0. Retrieved from https://www.eclipse.org/legal/epl-v10.html.Google Scholar
- Ghostery. Retrieved from https://www.ghostery.com.Google Scholar
- GNU Linear Programming Kit, (GLPK). Retrieved from http://www.gnu.org/software/glpk.Google Scholar
- IAB Quality Assurance Guidelines (QAG) Taxonomy. Retrieved from http://www.iab.com/guidelines/iab-quality-assurance-guidelines-qag-taxonomy/. Accessed on 2015-09-11Google Scholar
- Lightbeam. Retrieved from https://www.mozilla.org/en-US/lightbeam/.Google Scholar
- The Official EasyList Website. Retrieved from https://easylist.adblockplus.org. Accessed on 2015-10-22.Google Scholar
- Privacy Badger. https://www.eff.org/es/node/73969.Google Scholar
- Real-Time Bidding Protocol-Cookie Matching. Retrieved from https://developers.google.com/ad-exchange/rtb/cookie-guide. Accessed on 2015-10-07.Google Scholar
- Real-Time Bidding Protocol - Processing the Request. Retrieved from https://developers.google.com/ad-exchange/rtb/request-guide. Accessed on 2015-10-07.Google Scholar
- Cisco 2009. Cisco Service Control Online Advertising Solution Guide. Technical Report. Cisco Syst.Google Scholar
- 2010. Evercookie-virtually irrevocable persistent cookies. Retrieved from http://samy.pl/evercookie. Oct. 2010.Google Scholar
- 2010. Topline U.S. Web Data for March 2010. Technical Report. Retrieved from http://www.nielsen.com/us/en/insights/news/2010/nielsen-provides-topline-u-s-web-data-for-march-2010.html.Google Scholar
- 2011. Adblock Plus User Survey Results, Part 3. Technical Report. Eyeo. Retrieved from https://adblockplus.org/blog/adblock-plus-user-survey-results-part-3.Google Scholar
- 2012. The State of Online Advertising. Technical Report. Adobe. Retrieved from http://www.adobe.com/aboutadobe/pressroom/pdfs/Adobe_State_of_Online_Adve rtising_Study.pdf. Accessed on 2015-09-11.Google Scholar
- 2014. Firefox Interest Dashboard. Retrieved from https://www.mozilla.org/en-US/firefox/interest-dashboard/.Google Scholar
- 2014. US Programmatic Ad Spend Tops $10 Billion This Year, to Double by 2016. Technical Report. eMarketer. Retrieved from http://www.emarketer.com/Article/US-Programmatic-Ad-Spend-Tops-10-Billion-This-Year-Double-by-2016/1011312Google Scholar
- 2015. The Cost of Ad Blocking. Res. rep. PageFair.Google Scholar
- 2015. Google DoubleClick Ad Exchange (AdX) Buyer Program Guidelines. Retrieved from http://www.google.com/doubleclick/adxbuyer/guidelines.html.Google Scholar
- 2015. Tracking Preference Expression (DNT). Technical Report. Retrieved from http://www.w3.org/TR/tracking-dnt/.Google Scholar
- G. Acar, C. Eubank, S. Englehardt, M. Juarez, A. Narayanan, and C. Diaz. 2014. The web never forgets: Persistent tracking mechanisms in the wild. In Proc. ACM Conf. Comput., Commun. Secur. (CCS). Washington, DC, 674--689.Google Scholar
- K. G. Allen. 2014. Search Marketing Tops Online Retail Customer Acquisition Tactics. Technical Report. NFR. Retrieved from https://nrf.com/media/press-releases/shoporgforrester-search-marketing-tops-online-retail-customer-acquisition.Google Scholar
- M. Aly, A. Hatch, V. Josifovski, and V. K. Narayanan. 2012. Web-scale user modeling for targeting. In Proc. Int. WWW Conf. ACM, 3--12. Google ScholarDigital Library
- AOL 2006. AOL Search Data Scandal. Retrieved from http://en.wikipedia.org/wiki/AOL_search_data_scandal.Google Scholar
- M. Arment. 2015. The ethics of modern web ad-blocking. Retrieved from (Aug. 2015). http://www.marco.org/2015/08/11/ad-blocking-ethics.Google Scholar
- P. Barford, I. Canadi, D. Krushevskaja, Q. Ma, and S. Muthukrishnan. 2014. Adscape: Harvesting and analyzing online display ads. In Proc. ACM Int. WWW Conf. ACM, 597--608.Google Scholar
- S. J. Benson, Y. Ye, and X. Zhang. 2000. Solving large-scale sparse semidefinite programs for combinatorial optimization. (SIAM) J. Optim. 10, 2 (2000), 443--461.Google Scholar
- L. Bentivogli, P. Forner, B. Magnini, and E. Pianta. 2004. Revising WordNet domains hierarchy: Semantics, coverage, and balancing. In Proc. PostCOLING Workshop Multiling. Ling. Resources. 101--108.Google Scholar
- M. Berkelaar, K. Eikland, and P. Notebaert. 2004. Open Source (Mixed Integer) Linear Programming System. Retrieved from http://lpsolve.sourceforge.net.Google Scholar
- B. Borchers. 1999. CSDP, a C library for semidefinite programming. Optim. Method, Softw. 11, 1 (1999), 613--623. Google ScholarCross Ref
- S. Boyd and L. Vandenberghe. 2004. Convex Optimization. Cambridge University Press, Cambridge, UK. Google ScholarCross Ref
- J. Butler. 2010. Case Study: How Display Ad Remarketing Works in Travel. Technical Report. Tnooz. Retrieved from http://www.tnooz.com/article/case-study-how-display-ad-remarketing-works-in-travel/.Google Scholar
- J. M. Carrascosa, J. Mikians, R. Cuevas, V. Erramilli, and N. Laoutaris. 2014. Understanding interest-based behavioural targeted advertising. In arXiv: 1411.5281v1.Google Scholar
- J. M. Carrascosa, J. Mikians, R. Cuevas, V. Erramilli, and N. Laoutaris. 2015. I always feel like somebody’s watching me. Measuring online behavioural advertising. In Proc. ACM Int. Emerg. Netw. Experiments, Technol. (CoNEXT).Google Scholar
- R. Cookson. 2015. Google, Microsoft and Amazon pay to get around ad blocking tool. Retrieved from http://www.ft.com/cms/s/0/80a8ce54-a61d-11e4-9bd3-00144feab7de.html.Google Scholar
- T. M. Cover and J. A. Thomas. 2006. Elements of Information Theory (2nd ed.). Wiley, New York.Google Scholar
- J. Currie and D. I. Wilson. 2012. OPTI: Lowering the barrier between open source optimizers and the industrial MATLAB user. In Proc. Found. Comput.-Aided Process Oper.Google Scholar
- A. Datta, M. C. Tschantz, and A. Datta. 2015. Automated experiments on ad privacy settings. In Proc. Int. Symp. Priv. Enhanc. Technol. (PETS). Google ScholarCross Ref
- J. Daudé, L. Padró, and German Rigau. 2003. Validation and tuning of wordnet mapping techniques. Proc. Int. Conf. Recent Adv. Nat. Lang. Process. (RANLP) (Sept. 2003).Google Scholar
- W. Davis. 2015. FTC’s Julie Brill Tells Ad Tech Companies To Improve Privacy Protections. Retrieved from http://www.mediapost.com/publications/article/259210/ftcs-julie-brill-tells-ad-tech-companies-to-impro.html.Google Scholar
- S. Englehardt. 2014. The hidden perils of cookie syncing. Retrieved from https://freedom-to-tinker.com/blog/englehardt/the-hidden-perils-of-cookie-syncing/.Google Scholar
- E. Ferrari and B. Thuraisingham. 2000. Artech House, Inc., Chapter Secure Database Systems, 353--403.Google Scholar
- Floodwatch. Floodwatch. Retrieved from https://floodwatch.o-c-r.org/.Google Scholar
- J. Q. Freed. 2012. Hoteliers Rake in Returns Through Retargeting. Technical Report. Hotel News Now. Retrieved from http://www.hotelnewsnow.com/Article/7710/Hoteliers-rake-in-returns-through-retargeting.Google Scholar
- S. Gauch, M. Speretta, A. Chandramouli, and A. Micarelli. 2007. User profiles for personalized information access, in The Adaptive Web. Springer-Verlag, 54--89.Google Scholar
- E. M. Gertz and S. J. Wright. 2003. Object-oriented software for quadratic programming. ACM Trans. Math. Softw. 29 (2003), 58--81. Google ScholarDigital Library
- A. Gonzalez-Agirre, E. Laparra, and G. Rigau. 2012. Multilingual central repository version 3.0: Upgrading a very large lexical knowledge base. In Proc. Global WordNet Conf.Google Scholar
- S. Guha, B. Cheng, and P. Francis. 2010. Challenges in measuring online advertising systems. In Proc. ACM Internet Meas. Conf. (IMC). Google ScholarDigital Library
- M. Gundlach. AdBlock. Retrieved from https://getadblock.com/.Google Scholar
- B. J. Jansen. 2007. Click fraud. IEEE Comput. 40, 7 (July 2007), 85--86. Google ScholarDigital Library
- E. T. Jaynes. 1957. Information theory and statistical mechanics II. Phys. Review Ser. II 108, 2 (1957), 171--190. Google ScholarCross Ref
- E. T. Jaynes. 1982. On the rationale of maximum-entropy methods. Proc. IEEE 70, 9 (Sept. 1982), 939--952. Google ScholarCross Ref
- S. G. Johnson. NLopt nonlinear-optimization package. Retrieved from http://ab-initio.mit.edu/nlopt.Google Scholar
- A. Kae, K. Kan, V. K. Narayanan, and D. Yankov. 2011. Categorization of display ads using image and landing page features. In Proc. ICDM Workshop Large-Scale Data Min.: Theory, Appl. ACM, 1--8. http://doi.acm.org/10.1145/2002945.2002946. Google ScholarDigital Library
- T. Kawaja. 2015. Display LUMAscape. Retrieved from http://www.lumapartners.com/lumascapes/display-ad-tech-lumascape.Google Scholar
- P. Kouvelis and G. Yu. 1996. Robust Discrete Optimization and Its Applications (1st ed.). Springer-Verlag.Google Scholar
- M. Lecuyer, G. Ducoffe, F. Lan, A. Papancea, T. Petsios, R. Spahn, A. Chaintreau, and R. Geambasu. 2014. XRay: Enhancing the web’s transparency with differential correlation. In Proc. Conf. USENIX Secur. Symp.Google Scholar
- M. Lecuyer, R. Spahn, Y. Spiliopoulos, A. Chaintreau, R. Geambasu, and D. Hsu. 2015. Sunlight: Finegrained targeting detection at scale with statistical confidence. In Proc. ACM Conf. Comput., Commun. Secur. (CCS).Google Scholar
- B. C. Levy. 2008. Principles of Signal Detection and Parameter Estimation (1st ed.). Springer-Verlag. Google ScholarCross Ref
- B. Liu, A. Sheth, U. Weinsberg, J. Chandrashekar, and R. Govindan. 2013. AdReveal: Improving transparency into online targeted advertising. In Proc. Hot Topics in Netw. ACM, 121--127. Google ScholarDigital Library
- R. Lougee-Heimer. 2003. The common optimization INterface for operations research: Promoting open-source software in the operations research community. IMB J. Res. Develop. 47, 1 (Jan. 2003), 57--66. Google ScholarDigital Library
- B. Magnini and G. Cavaglià. 2000. Integrating subject field codes into WordNet. In Proc. Lang. Resource, Evaluation (LREC). 1413--1418.Google Scholar
- C. D. Manning and H. Schütze. 1999. Foundations of Statistical Natural Language Processing. MIT Press, Cambridge, MA.Google Scholar
- B. Markines, C. Cattuto, F. Menczer, D. Benz, A. Hotho, and G. Stum. 2009. Evaluating similarity measures for emergent semantics of social tagging. In Proc. Int. WWW Conf. ACM, 641--650. Google ScholarDigital Library
- G. Marvin. 2013. Consumers Now Notice Retargeted Ads. Technical Report. Marketing Land. Retrieved from http://marketingland.com/3-out-4-consumers-notice-retargeted-ads-67813.Google Scholar
- W. Melicher, M. Sharif, J. Tan, L. Bauer, M. Christodorescu, and P. G. Leon. 2016. (Do not) track me sometimes: Users’ contextual preferences for web tracking. In Proc. Int. Symp. Priv. Enhanc. Technol. (PETS), Lecture Notes in Computer Science. Springer-Verlag, 1--20.Google Scholar
- G. A. Miller. 1995. WordNet: A lexical database for english. Commun. ACM 38, 11 (1995), 39--41. Google ScholarDigital Library
- T. Morey, T. Forbath, and A. Schoop. 2015. Customer Data: Designing for Transparency and Trust. Internet draft. Retrieved from https://hbr.org/2015/05/customer-data-designing-for-transparency-and-trust.Google Scholar
- K. Mowery and H. Shacham. 2012. Pixel perfect: Fingerprinting canvas in HTML5. In Proc. IEEE Web 2.0 Workshop Secur., Priv. (W2SP). IEEE Comput. Soc.Google Scholar
- J. Naughton. 2015. The rise of ad-blocking could herald the end of the free internet. Retrieved from http://www.theguardian.com/commentisfree/2015/sep/27/ad-blocking-herald-end-of-free-internet-ios9-apple.Google Scholar
- T.-D. Nguyen. 2009. Robust Estimation, Regression and Ranking with Applications in Portfolio Optimization. Ph.D. dissertation. MIT.Google Scholar
- L. Olejnik. 2015. Measuring the Privacy Risks and Value of Web Tracking. Ph.D. dissertation.Google Scholar
- L. Olejnik, T. Minh-Dung, and C. Castelluccia. 2014. Selling off privacy at auction. In Proc. Symp. Netw. Distrib. Syst. Secur. (SNDSS). Internet. Soc. Google ScholarCross Ref
- S. Pandey, M. Aly, A. Bagherjeiran, A. Hatch, P. Ciccolo, A. Ratnaparkhi, and M. Zinkevich. 2011. Learning to target: What works for behavioral targeting. In Proc. Int. Conf. Inform., Knowl. Manage. (CIKM). ACM, 1805--1814.Google Scholar
- J. Parra-Arnau, D. Rebollo-Monedero, and J. Forné. 2014. Measuring the privacy of user profiles in personalized information systems. Future Gen. Comput. Syst. (FGCS), Special Issue Data, Knowl. Eng. 33 (April 2014), 53--63. http://dx.doi.org/10.1016/j.future.2013.01.001 Google ScholarDigital Library
- S. Puglisi, D. Rebollo-Monedero, and J. Forné. 2015. You never surf alone. Ubiquitous tracking of users’ browsing habits. In Proc. Int. Workshop Data Priv. Manage. (DPM), Lecture Notes in Computer Science Vol. 9481.Google Scholar
- K. Purcell, J. Brenner, and Lee Rainie. 2012. Search Engine Use 2012. Res. rep. Pew Internet, Amer. Life Project.Google Scholar
- D. Rebollo-Monedero, J. Parra-Arnau, and J. Forné. 2011. An information-theoretic privacy criterion for query forgery in information retrieval. In Proc. Int. Conf. Secur. Technol. (SecTech) (Commun. Comput., Inform. Sci. (CCIS)), Vol. 259. Springer-Verlag, 146--154. Google ScholarCross Ref
- D. Rogers. 2015. How Business Can Gain Consumers’ Trust Around Data. Retrieved from http://www.forbes.com/sites/davidrogers/2015/11/02/how-business-can-gain-consumers-trust-around-data/.Google Scholar
- G. Salton, A. Wong, and C. S. Yang. 1975. A vector space model for automatic indexing. Commun. ACM 18, 11 (1975), 613--620. Google ScholarDigital Library
- P. Sayer. 2015. Adblock extension begins whitelisting “acceptable ads.” Retrieved from http://www.pcworld.com/article/2988838.Google Scholar
- M. J. Schervish. 1995. Theory of Statistics. Springer-Verlag, New York. Google ScholarCross Ref
- M. Smith. 2014. Targeted: How Technology Is Revolutionizing Advertising and the Way Companies Reach Consumers (1st ed.). AMACOM, New York.Google Scholar
- A. Soltani, S. Canty, Q. Mayo, L. Thomas, and C. J. Hoofnagle. 2010. Flash cookies and privacy. In Proc. AAAI Spring Symp. Intell. Inform. Priv. Manage. Assoc. Adv. Artif. Intell.Google Scholar
- S. Thielman. 2015. Rise of ad-blockers shows advertising does not understand mobile, say experts. Retrieved from http://www.theguardian.com/technology/2015/oct/03/ad-blockers-advertising-mobile-apple.Google Scholar
- V. Toubiana. 2007. SquiggleSR. Retrieved from www.squigglesr.com.Google Scholar
- V. Toubiana, A. Narayanan, D. Boneh, H. Nissenbaum, and S. Barocas. 2010. Adnostic: Privacy preserving targeted advertising. In Proc. Symp. Netw. Distrib. Syst. Secur. (SNDSS). 1--21.Google Scholar
- M. M. Tsang, S. C. Ho, and T. P. Liang. 2004. Consumer attitudes toward mobile advertising: An empirical study. Int. J. Electron. Commer. 8, 3 (2004), 65--78.Google ScholarDigital Library
- A. Wächter and L. T. Biegler. 2006. On the implementation of a primal-dual interior point filter line search algorithm for large-scale nonlinear programming. Math. Program. 106, 1 (2006), 25--57. Google ScholarDigital Library
- V. Woollaston. 2015. Facebook slammed after advertising funeral directors to a cancer patient. Retrieved from http://www.dailymail.co.uk/sciencetech/article-2989768.Google Scholar
- J. Yan, N. Liu, G. Wang, W. Zhang, Y. Jiang, and Z. Chen. 2009. How much can behavioral targeting help online advertising? In Proc. Int. WWW Conf. ACM, 261--270. Google ScholarDigital Library
- K. Yang, Y. Wu, J. Huang, X. Wang, and S. Verdu. 2008. Distributed robust optimization for communication networks. In Proc. Joint Conf. IEEE Comput., Commun. Soc. (INFOCOM). Google ScholarCross Ref
- YourOnlineChoices. YourOnlineChoices. Retrieved from http://www.youronlinechoices.com/.Google Scholar
- S. Yuan, A. Z. Abidin, M. Sloan, and J. Wang. 2012. Internet advertising: An interplay among advertisers, online publishers, ad exchanges and web users. arXiv: 1206.1754 .Google Scholar
- C. Zhu, R. H. Byrd, and J. Nocedal. 2007. L-BFGS-B: Algorithm 778: L-BFGS-B FORTRAN routines for large scale bound constrained optimization. ACM Trans. Math. Softw. 23, 4 (2007), 550--560. Google ScholarDigital Library
- A. M. Zoubir, V. Koivunen, and Y. Chakhchoukh M. Muma. 2012. Robust estimation in signal processing: A tutorial-style treatment of fundamental concepts. IEEE Signal Process. Mag. 29, 4 (July 2012), 61--80. Google ScholarCross Ref
Index Terms
- MyAdChoices: Bringing Transparency and Control to Online Advertising
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