research-article

In-Depth Survey of Digital Advertising Technologies

Authors:

A. Selcuk Uluagac,

John A. CopelandAuthors Info & Claims

IEEE Communications Surveys & Tutorials, Volume 18, Issue 3

Pages 2124 - 2148

https://doi.org/10.1109/COMST.2016.2519912

Published: 01 July 2016 Publication History

Abstract

Some of the world’s most well-known IT companies are in fact advertising companies deriving their primary revenues through digital advertising. For this reason, these IT giants are able to continually drive the evolutions of information technology in ways that serve to enhance our everyday lives. The benefits of this relationship include free web browsers with powerful search engines and mobile applications. Still, it turns out that “free” comes at a cost that is paid through our interactions within a digital advertising ecosystem. Digital advertising is not without its challenges. Issues originate from the complex platforms utilized to support advertising over web and mobile application interfaces. This is especially true for advertising links. Additionally, as new methods for advertising develop so too does the potential for impacting its underlying ecosystem for good or ill. Accordingly, researchers are interested in understanding this ecosystem, the factors that impact it, and the strategies for improving it. The major contribution of this survey is that it is the first review of the digital advertising ecosystem as it applies to online websites and mobile applications. In doing so, we explain the digital advertising relationships within this ecosystem along with their technical, social, political, and physical implications. Furthermore, advertising principles along with a variation of other advertising approaches, both legitimate and malicious, are explored in order to compare and contrast competing digital advertising methods.

References

[1]

L. Goldberg and S. Silber. (Jun. 2014). “At $11.6 billion in q1 2014, internet advertising revenues hit all-time first quarter high.” [Online]. Available: http://www.iab.com/at-11-6-billion-in-q1-2014-internet-advertisingrevenues-hit-all-time-first-quarter-high/,

[2]

L. Goldberg and S. Silber. (Dec. 2014). “Q3 2014 internet advertising revenues hit $12.4 billion, making it the highest quarter on record.” [Online]. Available: http://www.iab.com/q3-2014-internetadvertising-revenues-hit-12-4-billion-making-it-the-highest-quarter-onrecord/

[3]

G. Dunn. (2013). Cyber-Security and Data Privacy Outlook and Review [Online]. Available: http://www.gibsondunn.com/publications/pages/Cyber-security-and-Data-Privacy-Outlook-and-Review-2013.aspx

[4]

V. Dave, S. Guha, and Y. Zhang, “ViceROI: Catching click-spam in search ad networks,” in Proc. ACM SIGSAC Conf. Comput. Commun. Secur., 2013, pp. 765–776.

[5]

Y. Wang, D. Burgener, A. Kuzmanovic, and G. Maciá-Fernández, “Understanding the network and user-targeting properties of web advertising networks,” in Proc. 31st Int. Conf. Distrib. Comput. Syst. (ICDCS), 2011, pp. 613–622.

[6]

S. Muthukrishnan, “Ad Exchanges: Research Issues,” in Proc. Internet Netw. Econ., 2009, pp. 1–12.

[7]

R. Gomes and V. Mirrokni, “Optimal revenue-sharing double auctions with applications to ad exchanges,” in Proc. 23rd Int. Conf. World Wide Web, 2014, pp. 19–28.

[8]

B. Stone-Gross, R. Stevens, A. Zarras, R. Kemmerer, C. Kruegel, and G. Vigna, “Understanding fraudulent activities in online ad exchanges,” in Proc. ACM SIGCOMM Conf. Internet Meas. Conf., 2011, pp. 279–294.

[9]

S. Angel and M. Walfish, “Verifiable auctions for online ad exchanges,” in Proc. ACM Conf. SIGCOMM (SIGCOMM13), 2013, pp. 195–206.

[10]

A. O. Insider. (2013, Aug.) How Ad Serving Works [Online]. Available: http://www.adopsinsider.com/ad-serving/how-ad-serving-works-mobile-vs-web-environments/

[11]

C. Pettey and R. van der Meulen. (Jan. 2013). Global Mobile Ad Market Growing Faster Than Expected, will be Worth us $11.4 Billion in 2013, Double That by 2016, Says Gartner [Online]. Available: http://www.gartner.com/newsroom/id/2306215.

[12]

S. Nath, F. X. Lin, L. Ravindranath, and J. Padhye, “SmartAds: Bringing contextual ads to mobile apps,” in Proc. 11th Annu. Int. Conf. Mobile Syst. Appl. Serv., 2013, pp. 111–124.

[13]

W. Enck et al., “Understanding android security,” IEEE Secur. Privacy, vol. 7, no. 1, pp. 50–57, Jan./Feb. 2009.

Digital Library

[14]

A. P. Felt, H. J. Wang, A. Moshchuk, S. Hanna, and E. Chin, “Permission re-delegation: Attacks and defenses,” in Proc. USENIX Secur. Symp., vol. 30, 2011.

[15]

A. Shabtai, Y. Fledel, U. Kanonov, Y. Elovici, S. Dolev, and C. Glezer, “Google android: A comprehensive security assessment,” IEEE Secur. Privacy, vol. 8, no. 2, pp. 35–44, Mar./Apr. 2010.

Digital Library

[16]

A. P. Felt, E. Chin, S. Hanna, D. Song, and D. Wagner, “Android permissions demystified,” in Proc. 18th ACM Conf. Comput. Commun. Secur., 2011, pp. 627–638.

[17]

K. W. Y. Au, Y. F. Zhou, Z. Huang, and D. Lie, “PScout: Analyzing the android permission specification,” in Proc. ACM Conf. Comput. Commun. Secur., 2012, pp. 217–228.

[18]

M. Dietz, S. Shekhar, Y. Pisetsky, A. Shu, and D. S. Wallach, “Quire: Lightweight provenance for smart phone operating systems,” in Proc. USENIX Secur. Symp., vol. 31, 2011.

[19]

T. Wang, K. Lu, L. Lu, S. Chung, and W. Lee, “Jekyll on iOS: When benign apps become evil,” in Usenix Security, inpresented as part of the 22nd USENIX Security Symposium (USENIX Security 13). Washington, D.C., USA: USENIX, vol. 13, 2013, pp. 559–572.

[20]

B. Lau, Y. Jang, and C. Song, “Mactans: Injecting malware into iOS devices via malicious chargers,” in Proc. Black Hat USA, 2013.

[21]

W. Enck et al., “TaintDroid: An information flow tracking system for real-time privacy monitoring on smartphones,” Commun. ACM, vol. 57, no. 3, pp. 99–106, 2014.

Digital Library

[22]

M. Egele, C. Kruegel, E. Kirda, and G. Vigna, “PiOS: Detecting privacy leaks in iOS applications,” in Proc. Netw. Distrib. Syst. Secur. Symp. (NDSS), Feb. 2011.

[23]

W. Enck, D. Octeau, P. McDaniel, and S. Chaudhuri, “A study of android application security,” in Proc. USENIX Secur. Symp., 2011, vol. 2, p. 2.

[24]

E. Smith. (2010). iPhone Applications & Privacy Issues: An Analysis of Application Transmission of iPhone Unique Device Identifiers (UDIDs) [Online]. Available: URL www.pskl.us/wp/wp-content/uploads/2010/09/iPhone-Applications-Privacy-Issues.pdf

[25]

E. Smith. (2012, Sep.) Apple’s UDID Hypocrisy [Online]. Available: http://www.pskl.us/wp/?p=805

[26]

A. Pathak, Y. C. Hu, and M. Zhang, “Where is the energy spent inside my app?: Fine grained energy accounting on smartphones with Eprof,” in Proc. 7th ACM Eur. Conf. Comput. Syst., 2012, pp. 29–42.

[27]

M. C. Grace, W. Zhou, X. Jiang, and A.-R. Sadeghi, “Unsafe exposure analysis of mobile in-app advertisements,” in Proc. 5th ACM Conf. Secur. Privacy Wireless Mobile Netw., 2012, pp. 101–112.

[28]

T. Luo, H. Hao, W. Du, Y. Wang, and H. Yin, “Attacks on webview in the android system,” in Proc. 27th Annu. Comput. Secur. Appl. Conf., 2011, pp. 343–352.

[29]

R. Stevens, C. Gibler, J. Crussell, J. Erickson, and H. Chen, “Investigating user privacy in android ad libraries,” in Proc. Workshop Mobile Secur. Technol. (MoST), San Francisco, CA, USA, 2012.

[30]

A. J. Khan, V. Subbaraju, A. Misra, and S. Seshan, “Mitigating the true cost of advertisement-supported free mobile applications,” in Proc. 12th Workshop Mobile Comput. Syst. Appl., 2012, p. 1.

[31]

S. Dai, A. Tongaonkar, X. Wang, A. Nucci, and D. Song, “Networkprofiler: Towards automatic fingerprinting of android apps,” in Proc. INFOCOM, 2013, pp. 809–817.

[32]

C. Gibler, R. Stevens, J. Crussell, H. Chen, H. Zang, and H. Choi, “Adrob: Examining the landscape and impact of android application plagiarism,” in Proc. 11th Annu. Int. Conf. Mobile Syst. Appl. Serv., 2013, pp. 431–444.

[33]

T. Book, A. Pridgen, and D. S. Wallach, “Longitudinal analysis of android ad library permissions,” arXiv preprint arXiv:1303.0857, 2013.

[34]

Y. Chen, S. Zhu, H. Xu, and Y. Zhou, “Children’s exposure to mobile in-app advertising: An analysis of content appropriateness,” in Proc. Int. Conf. Social Comput. (SocialCom), 2013, pp. 196–203.

[35]

N. Vallina-Rodriguez et al., “Breaking for commercials: Characterizing mobile advertising,” in Proc. ACM Conf. Internet Meas. Conf., 2012, pp. 343–356.

[36]

V. Svajcer and S. McDonald, “Classifying PUAs in the mobile environment,” in Proc. Virus Bull. Conf., VB2013, Berlin, Germany, pp. 139–147, Oct. 2013.

[37]

H. Haddadi, P. Hui, and I. Brown, “MobiAd: Private and scalable mobile advertising,” in Proc. 5th ACM Int. Workshop Mobility Evolv. Internet Architect., 2010, pp. 33–38.

[38]

A. Tongaonkar, S. Dai, A. Nucci, and D. Song, “Understanding mobile app usage patterns using in-app advertisements,” in Passive and Active Measurement. New York, NY, USA: Springer, 2013, pp. 63–72.

[39]

S. Shekhar, M. Dietz, and D. S. Wallach, “Adsplit: Separating smartphone advertising from applications,” in Proc. USENIX Secur. Symp., 2012, pp. 553–567.

[40]

T. Book and D. S. Wallach, “A case of collusion: A study of the interface between ad libraries and their apps,” in Proc. 3rd ACM Workshop Secur. Privacy Smartphones Mobile Devices, 2013, pp. 79–86.

[41]

Q. Zhang, T. Ristenpart, S. Savage, and G. M. Voelker, “Got traffic?: An evaluation of click traffic providers,” in Proc. Joint WICOW/AIRWeb Workshop Web Qual., 2011, pp. 19–26.

[42]

O. Chapelle, “Modeling delayed feedback in display advertising,” in Proc. 20th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., 2014, pp. 1097–1105.

[43]

P. Gill, V. Erramilli, A. Chaintreau, B. Krishnamurthy, K. Papagiannaki, and P. Rodriguez, “Best paper–Follow the money: Understanding economics of online aggregation and advertising,” in Proc. Conf. Internet Meas. Conf., 2013, pp. 141–148.

[44]

H. Beales. (Mar. 2010). “The value of behavioral targeting,” Network Advertising Initiative. [Online]. Available: http://www.networkadvertising.org/pdfs/Beales NAI Study.pdf.

[45]

G. Buscher, S. T. Dumais, and E. Cutrell, “The good, the bad, and the random: An eye-tracking study of ad quality in web search,” in Proc. 33rd Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2010, pp. 42–49.

[46]

A. Hunter, M. Jacobsen, R. Talens, and T. Winders, “When money moves to digital, where should it go? Identifying the right media-placement strategies for digital display,” White paper, comScore and ValueClick Media, Sep. 2010 [Online]. Available: www.recruemedia.com/downloads/comscore2010.pdf, accessed on Jun. 19, 2013).

[47]

W. Zhang, S. Yuan, and J. Wang, “Optimal real-time bidding for display advertising,” in Proc. 20th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., 2014, pp. 1077–1086.

[48]

A. Broder, E. Gabrilovich, V. Josifovski, G. Mavromatis, and A. Smola, “Bid generation for advanced match in sponsored search,” in Proc. 4th ACM Int. Conf. Web Search Data Min., 2011, pp. 515–524.

[49]

H. B. McMahan et al., “Ad click prediction: A view from the trenches,” in Proc. 19th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., 2013, pp. 1222–1230.

[50]

W. Heavlin and A. Radovanovic, “Risk-aware revenue maximization in display advertising,” in Proc. 21st Int. Conf. World Wide Web (WWW’12), New York, NY, USA: ACM, 2012, pp. 91–100.

[51]

N. Alon, I. Gamzu, and M. Tennenholtz, “Optimizing budget allocation among channels and influencers,” in Proc. 21st Int. Conf. World Wide Web, 2012, pp. 381–388.

Digital Library

[52]

L. C. Stavrogiannis, E. H. Gerding, and M. Polukarov, “Auction mechanisms for demand-side intermediaries in online advertising exchanges,” in Proc. Int. Conf. Auton. Agents Multi-agent Syst., 2014, pp. 1037–1044.

[53]

B. Lucier, R. Paes Leme, and E. Tardos, “On revenue in the generalized second price auction,” in Proc. 21st Int. Conf. World Wide Web, 2012, pp. 361–370.

Digital Library

[54]

B. Edelman, M. Ostrovsky, and M. Schwarz, “Internet advertising and the generalized second price auction: Selling billions of dollars worth of keywords,” Am. Econ. Rev., vol. 97, no. 1, pp. 242–259, 2007.

[55]

M. Bendersky, E. Gabrilovich, V. Josifovski, and D. Metzler, “The anatomy of an ad: Structured indexing and retrieval for sponsored search,” in Proc. 19th Int. Conf. World Wide Web, 2010, pp. 101–110.

[56]

A. Ghosh and A. Sayedi, “Expressive auctions for externalities in online advertising,” in Proc. 19th Int. Conf. World Wide Web, 2010, pp. 371–380.

Digital Library

[57]

L. E. Celis, G. Lewis, M. M. Mobius, and H. Nazerzadeh, “Buy-it-now or take-a-chance: A simple sequential screening mechanism,” in Proc. 20th Int. Conf. World Wide Web, 2011, pp. 147–156.

[58]

P. Dütting, M. Henzinger, and I. Weber, “An expressive mechanism for auctions on the web,” in Proc. 20th Int. Conf. World Wide Web, 2011, pp. 127–136.

[59]

S. Hua, X. Zhuo, and S.S. Panwar, “A truthful auction based incentive framework for femtocell access,” in Proc. Wirel. Commun. Netw. Conf. (WCNC), 2013, pp. 2271–2276.

[60]

A. Korolova, “Privacy violations using microtargeted ads: A case study,” in Proc. IEEE Int. Conf. Data Min. Workshops (ICDMW), 2010, pp. 474–482.

[61]

A. Nath, S. Mukherjee, P. Jain, N. Goyal, and S. Laxman, “Ad impression forecasting for sponsored search,” in Proc. 22nd Int. Conf. World Wide Web, 2013, pp. 943–952.

Digital Library

[62]

D. G. Goldstein, R. P. McAfee, and S. Suri, “The cost of annoying ads,” in Proc. 22nd Int. Conf. World Wide Web, 2013, pp. 459–470.

Digital Library

[63]

H. Bao and E. Y. Chang, “Adheat: An influence-based diffusion model for propagating hints to match ads,” in Proc. 19th Int. Conf. World Wide Web, 2010, pp. 71–80.

[64]

Y. Choi, M. Fontoura, E. Gabrilovich, V. Josifovski, M. Mediano, and B. Pang, “Using landing pages for sponsored search ad selection,” in Proc. 19th Int. Conf. World Wide Web, 2010, pp. 251–260.

Digital Library

[65]

R. Agrawal, A. Gupta, Y. Prabhu, and M. Varma, “Multi-label learning with millions of labels: Recommending advertiser bid phrases for web pages,” in Proc. 22nd Int. Conf. World Wide Web, 2013, pp. 13–24.

Digital Library

[66]

K. J. Lang, B. Moseley, and S. Vassilvitskii, “Handling forecast errors while bidding for display advertising,” in Proc. 21st Int. Conf. World Wide Web, 2012, pp. 371–380.

Digital Library

[67]

M. Bilenko and M. Richardson, “Predictive client-side profiles for personalized advertising,” in Proc. 17th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., 2011, pp. 413–421.

[68]

D. Agarwal, S. Pandey, and V. Josifovski, “Targeting converters for new campaigns through factor models,” in Proc. 21st Int. Conf. World Wide Web, 2012, pp. 101–110.

Digital Library

[69]

N. Archak, V. S. Mirrokni, and S. Muthukrishnan, “Mining advertiser-specific user behavior using adfactors,” in Proc. 19th Int. Conf. World wide web, 2010, pp. 31–40.

Digital Library

[70]

R. A. Lewis, J. M. Rao, and D. H. Reiley, “Here, there, and everywhere: Correlated online behaviors can lead to overestimates of the effects of advertising,” in Proc. 20th Int. Conf. World Wide Web, 2011, pp. 157–166.

[71]

A. Farahat and M. C. Bailey, “How effective is targeted advertising?” in Proc. 21st Int. Conf. World Wide Web, 2012, pp. 111–120.

Digital Library

[72]

M. Meyer, M. Balsam, A. O’Keeffe, C. Schlüter, and J. Fendler, “Admotional: Towards personalized online ads,” IJCSA, vol. 8, no. 2, pp. 59–80, 2011.

[73]

I. Leontiadis, C. Efstratiou, M. Picone, and C. Mascolo, “Don’t kill my ads!: Balancing privacy in an ad-supported mobile application market,” in Proc. 12th Workshop Mobile Comput. Syst. Appl., 2012, p. 2.

[74]

I. Obermiller and S. Bayless, “Legends of descent: Analytics in an ad-supported windows phone game,” in Proc. 1st ACM Int. Workshop Mobile Gaming, 2012, pp. 1–6.

[75]

T. Petsas, A. Papadogiannakis, M. Polychronakis, E. P. Markatos, and T. Karagiannis, “Rise of the planet of the apps: A systematic study of the mobile app ecosystem,” in Proc. Conf. Internet Meas. Conf., 2013, pp. 277–290.

[76]

X. Wei, L. Gomez, I. Neamtiu, and M. Faloutsos, “Profiledroid: Multi-layer profiling of android applications,” in Proc. 18th Annu. Int. Conf. Mobile Comput. Netw., 2012, pp. 137–148.

[77]

L. Zhang, D. Gupta, and P. Mohapatra, “How expensive are free smartphone apps?” in Proc. ACM SIGMOBILE Mobile Comput. Commun. Rev., 2012, vol. 16, no. 3, pp. 21–32.

[78]

W. Zhou, Y. Zhou, X. Jiang, and P. Ning, “Detecting repackaged smartphone applications in third-party android marketplaces,” in Proc. 2nd ACM Conf. Data Appl. Secur. Privacy, 2012, pp. 317–326.

[79]

W. Zhou, Y. Zhou, M. Grace, X. Jiang, and S. Zou, “Fast, scalable detection of piggybacked mobile applications,” in Proc. 3rd ACM Conf. Data Appl. Secur. Privacy, 2013, pp. 185–196.

[80]

F. Roesner, T. Kohno, and D. Wetherall, “Detecting and defending against third-party tracking on the web,” in Proc. 9th USENIX Conf. Netw. Syst. Des. Implement. (NSDI’12), 2012, p. 12 [Online]. Available: http://dl.acm.org/citation.cfm?id=2228298.2228315

Digital Library

[81]

S. Ihm and V. S. Pai, “Towards understanding modern web traffic,” in Proc. ACM SIGCOMM Conf. Internet Meas. Conf., 2011, pp. 295–312.

[82]

S. S. Krishnan and R. K. Sitaraman, “Understanding the effectiveness of video ads: A measurement study,” in Proc. Internet Meas. Conf., 2013, pp. 149–162.

[83]

S. Guha, B. Cheng, and P. Francis, “Challenges in measuring online advertising systems,” in Proc. 10th ACM SIGCOMM Conf. Internet Meas., 2010, pp. 81–87.

[84]

R. Balebako, P. Leon, R. Shay, B. Ur, Y. Wang, and L. Cranor, “Measuring the effectiveness of privacy tools for limiting behavioral advertising,” in Proc. Web 2.0 Workshop Secur. Privacy, 2012.

[85]

S. Han, J. Jung, and D. Wetherall, “A study of third-party tracking by mobile apps in the wild,” Univ. Washington, Tech. Rep. UW-CSE-12-03-01, 2012.

[86]

S. Sprankel, “Online tracking, targeted advertising and user privacy-the technical part,” 2011.

[87]

M. Bilenko, M. Richardson, and J. Y. Tsai, “Targeted, not tracked: Client-side solutions for privacy-friendly behavioral advertising,” in Proc. 11th Privacy Enhancing Technol. Symp. (PETS11), Sep. 2011.

[88]

J. R. Mayer and J. C. Mitchell, “Third-party web tracking: Policy and technology,” in Proc. IEEE Symp. Secur. Privacy (SP), 2012, pp. 413–427.

[89]

P. Barford, I. Canadi, D. Krushevskaja, Q. Ma, and S. Muthukrishnan, “Adscape: Harvesting and analyzing online display ads,” in Proc. 23rd Int. Conf. World Wide Web, 2014, pp. 597–608.

[90]

P. Eckersley, “How unique is your web browser?” in Privacy Enhancing Technologies. New York, NY, USA: Springer, 2010, pp. 1–18.

[91]

T.-F. Yen, Y. Xie, F. Yu, R. P. Yu, and M. Abadi, “Host fingerprinting and tracking on the web: Privacy and security implications,” in Proc. 19th Annu. Netw. Distrib. Syst. Secur. Symp. (NDSS), 2012.

[92]

S. Raju and R. Udupa, “Extracting advertising keywords from url strings,” in Proc. 21st Int. Conf. Companion World Wide Web, 2012, pp. 587–588.

Digital Library

[93]

J. Chen and J. Stallaert, “An economic analysis of online advertising using behavioral targeting,” MIS Quart., vol. 38, no. 2, pp. 429–449, 2014.

Digital Library

[94]

H. Falaki, R. Mahajan, S. Kandula, D. Lymberopoulos, R. Govindan, and D. Estrin, “Diversity in smartphone usage,” in Proc. 8th Int. Conf. Mobile Syst. Appl. Serv., 2010, pp. 179–194.

[95]

A. J. Khan, K. Jayarajah, D. Han, A. Misra, R. Balan, and S. Seshan, “CAMEO: A middleware for mobile advertisement delivery,” in Proc. 11th Annu. Int. Conf. Mobile Syst. Appl. Serv., 2013, pp. 125–138.

[96]

H. Kuzuno and S. Tonami, “Signature generation for sensitive information leakage in android applications,” in Proc. IEEE 29th Int. Conf. Data Eng. Workshops (ICDEW), 2013, pp. 112–119.

[97]

Q. Xu, J. Erman, A. Gerber, Z. Mao, J. Pang, and S. Venkataraman, “Identifying diverse usage behaviors of smartphone apps,” in Proc. ACM SIGCOMM Conf. Internet Meas. Conf., 2011, pp. 329–344.

[98]

C. Eubank, M. Melara, D. Perez-Botero, and A. Narayanan, “Shining the floodlights on mobile web tracking—A privacy survey,” in Proc. IEEE Workshop Web, vol. 2, 2013.

[99]

I. Polakis, S. Volanis, E. Athanasopoulos, and E. P. Markatos, “The man who was there: Validating check-ins in location-based services,” in Proc. 29th Annu. Comput. Secur. Appl. Conf., 2013, pp. 19–28.

[100]

J. Lindqvist, J. Cranshaw, J. Wiese, J. Hong, and J. Zimmerman, “I’m the mayor of my house: Examining why people use foursquare-a social-driven location sharing application,” in Proc. SIGCHI Conf. Hum. Factors Comput. Syst., 2011, pp. 2409–2418.

[101]

M. Backes, A. Kate, M. Maffei, and K. Pecina, “ObliviAd: Provably secure and practical online behavioral advertising,” in Proc. IEEE Symp. Secur. Privacy (SP), 2012, pp. 257–271.

[102]

A. Reznichenko, S. Guha, and P. Francis, “Auctions in do-not-track compliant internet advertising,” in Proc. 18th ACM Conf. Comput. Commun. Secur., 2011, pp. 667–676.

[103]

S. Guha, B. Cheng, and P. Francis, “Privad: Practical privacy in online advertising,” in Proc. Symp. Netw. Syst. Des. Implement. (NSDI), 2011, pp. 169–182.

[104]

S. Guha, A. Reznichenko, K. Tang, H. Haddadi, and P. Francis, “Serving ads from localhost for performance, privacy, and profit,” in Proc. HotNets, 2009.

[105]

H. Haddadi, S. Guha, and P. Francis, “Not all adware is badware: Towards privacy-aware advertising,” in Software Services for e-Business and e-Society. New York, NY, USA: Springer, 2009, pp. 161–172.

[106]

V. Toubiana, A. Narayanan, D. Boneh, H. Nissenbaum, and S. Barocas, “Adnostic: Privacy preserving targeted advertising,” in Proc. Netw. Distrib. Syst. Secur. (NDSS), 2010.

[107]

M. Fredrikson and B. Livshits, “Repriv: Re-imagining content personalization and in-browser privacy,” in Proc. IEEE Symp. Secur. Privacy (SP), 2011, pp. 131–146.

[108]

M. Dhawan, C. Kreibich, and N. Weaver, “Priv3: A third party cookie policy,” in Proc. W3C Workshop Do Not Track Beyond, Oct. 2012.

[109]

G. Kontaxis, M. Polychronakis, A. D. Keromytis, and E. P. Markatos, “Privacy-preserving social plugins,” in USENIX Security, in Presented as part of the 21st USENIX Security Symposium(USENIX Security 12). Bellevue, WA, USA: USENIX, 2012, pp. 631–646.

[110]

Fortune. (Feb. 2015). The World’s Most Admired Companies [Online]. Available: http://fortune.com/worlds-most-admired-companies/apple-1/

[111]

C. Castelluccia, M.-A. Kaafar, and M.-D. Tran, “Betrayed by your ads!: Reconstructing user profiles from targeted ads,” in Proc. 12th Int. Conf. Privacy Enhancing Technol. (PETS’12), 2012, pp. 1–17 [Online]. Available: https://doi.org/10.1007/978-3-642-31680-7_1

[112]

P. Pearce, A. P. Felt, G. Nunez, and D. Wagner, “Addroid: Privilege separation for applications and advertisers in android,” in Proc. 7th ACM Symp. Inf. Comput. Commun. Secur., 2012, pp. 71–72.

[113]

X. Zhang, A. Ahlawat, and W. Du, “AFrame: Isolating advertisements from mobile applications in android,” in Proc. 29th Annu. Comput. Secur. Appl. Conf., 2013, pp. 9–18.

[114]

P. Hornyack, S. Han, J. Jung, S. Schechter, and D. Wetherall, “These aren’t the droids you’re looking for: Retrofitting android to protect data from imperious applications,” in Proc. 18th ACM Conf. Comput. Commun. Secur., 2011, pp. 639–652.

[115]

E. Palme, B. Hess, and J. Sutanto, “Achieving targeted mobile advertisements while respecting privacy,” in Mobile Computing, Applications, and Services. New York, NY, USA: Springer, 2013, pp. 245–263.

[116]

A. Seneviratne, K. Thilakarathna, S. Seneviratne, M. A. Kaafar, and P. Mohapatra, “Reconciling bitter rivals: Towards privacy-aware and bandwidth efficient mobile ads delivery networks,” in Proc. 5th Int. Conf. Commun. Syst. Netw. (COMSNETS), 2013, pp. 1–10.

[117]

D. Davidson and B. Livshits, “MoRePriv: Mobile OS support for application personalization and privacy,” Microsoft Res., vol. 3, May 2012.

[118]

M. Hardt and S. Nath, “Privacy-aware personalization for mobile advertising,” in Proc. ACM Conf. Comput. Commun. Secur., 2012, pp. 662–673.

[119]

X. Wei, L. Gomez, I. Neamtiu, and M. Faloutsos, “Permission evolution in the android ecosystem,” in Proc. 28th Annu. Comput. Secur. Appl. Conf., 2012, pp. 31–40.

[120]

T. Micro. (2014). 12 Most Abused Android App Permissions [Online]. Available: http://about-threats.trendmicro.com/us/library/image-gallery/12-most-abused-android-app-permissions

[121]

D. Barrera, H. G. Kayacik, P. C. van Oorschot, and A. Somayaji, “A methodology for empirical analysis of permission-based security models and its application to android,” in Proc. 17th ACM Conf. Comput. Commun. Secur., 2010, pp. 73–84.

[122]

A. Vance. (2009, Sep.) Times Web Ads Show Security Breach [Online]. Available: http://www.nytimes.com/2009/09/15/technology/internet/15adco.html?_r=0

[123]

N. P. P. Mavrommatis and M. A. R. F. Monrose, “All your iFRAMEs point to us,” in Proc. 17th USENIX Secur. Symp., 2008, pp. 1–22.

[124]

Z. Li, K. Zhang, Y. Xie, F. Yu, and X. Wang, “Knowing your enemy: Understanding and detecting malicious web advertising,” in Proc. ACM Conf. Comput. Commun. Secur., 2012, pp. 674–686.

[125]

X. Dong, M. Tran, Z. Liang, and X. Jiang, “AdSentry: Comprehensive and flexible confinement of javascript-based advertisements,” in Proc. 27th Annu. Comput. Secur. Appl. Conf., 2011, pp. 297–306.

[126]

A. Zarras, A. Kapravelos, G. Stringhini, T. Holz, C. Kruegel, and G. Vigna, “The dark alleys of madison avenue: Understanding malicious advertisements,” in Proc. Conf. Internet Meas. Conf., 2014, pp. 373–380.

[127]

M. Ter Louw, K. T. Ganesh, and V. Venkatakrishnan, “AdJail: Practical enforcement of confidentiality and integrity policies on web advertisements,” in Proc. USENIX Secur. Symp., 2010, pp. 371–388.

[128]

S. Ford, M. Cova, C. Kruegel, and G. Vigna, “Analyzing and detecting malicious flash advertisements,” in Proc. Annu. Comput. Secur. Appl. Conf. (ACSAC’09), 2009, pp. 363–372.

[129]

V. Dave, S. Guha, and Y. Zhang, “Measuring and fingerprinting click-spam in ad networks,” in Proc. ACM Conf. Appl. Technol. Archit. Protoc. Comput. Commun. (SIGCOMM12), 2012, pp. 175–186.

[130]

S. A. Alrwais, A. Gerber, C. W. Dunn, O. Spatscheck, M. Gupta, and E. Osterweil, “Dissecting ghost clicks: Ad fraud via misdirected human clicks,” in Proc. 28th Annu. Comput. Secur. Appl. Conf., 2012, pp. 21–30.

[131]

H. Haddadi, “Fighting online click-fraud using bluff ads,” ACM SIGCOMM Comput. Commun. Rev., vol. 40, no. 2, pp. 21–25, 2010.

Digital Library

[132]

T. Blizard and N. Livic, “Click-fraud monetizing malware: A survey and case study,” in Proc. 7th Int. Conf. Malicious Unwanted Softw. (MALWARE), 2012, pp. 67–72.

[133]

B. Miller, P. Pearce, C. Grier, C. Kreibich, and V. Paxson, “What’s clicking what? Techniques and innovations of today’s clickbots,” in Detection of Intrusions and Malware, and Vulnerability Assessment. New York, NY, USA: Springer, 2011, pp. 164–183.

[134]

T. Moore and B. Edelman, “Measuring the perpetrators and funders of typosquatting,” in Financial Cryptography and Data Security. New York, NY, USA: Springer, 2010, pp. 175–191.

[135]

T. Moore, N. Leontiadis, and N. Christin, “Fashion crimes: Trending-term exploitation on the web,” in Proc. 18th ACM Conf. Comput. Commun. Secur., 2011, pp. 455–466.

[136]

W. Meng, X. Xing, A. Sheth, U. Weinsberg, and W. Lee, “Your online interests: Pwned! A pollution attack against targeted advertising,” in Proc. ACM SIGSAC Conf. Comput. Commun. Secur., 2014, pp. 129–140.

[137]

N. Nikiforakis et al., “Stranger danger: Exploring the ecosystem of ad-based url shortening services,” in Proc. 23rd Int. Conf. World Wide Web, 2014, pp. 51–62.

[138]

T. Vidas, D. Votipka, and N. Christin, “All your droid are belong to us: A survey of current android attacks,” in Proc. Usenix Conf. Offensive Technol. (WOOT), 2011, pp. 81–90.

[139]

A. P. Felt, M. Finifter, E. Chin, S. Hanna, and D. Wagner, “A survey of mobile malware in the wild,” in Proc. 1st ACM Workshop Secur. Privacy Smartphones Mobile Devices, 2011, pp. 3–14.

Digital Library

[140]

S. Rosen, Z. Qian, and Z. M. Mao, “AppProfiler: A flexible method of exposing privacy-related behavior in android applications to end users,” in Proc. 3rd ACM Conf. Data Appl. Secur. Privacy, 2013, pp. 221–232.

[141]

J. Hoffmann, M. Ussath, T. Holz, and M. Spreitzenbarth, “Slicing droids: Program slicing for smali code,” in Proc. 28th Annu. ACM Symp. Appl. Comput., 2013, pp. 1844–1851.

[142]

Y. Zhou and X. Jiang, “Dissecting android malware: Characterization and evolution,” in Proc. IEEE Symp. Secur. Privacy (SP), 2012, pp. 95–109.

[143]

Y. Zhang. (2015, Oct.) Kemoge: Another Mobile Malicious Adware Infecting Over 20 Countries [Online]. Available: https://www.fireeye.com/blog/threat-research/2015/10/kemoge_another_mobi.html

[144]

Y. Zhou, Z. Wang, W. Zhou, and X. Jiang, “Hey, you, get off of my market: Detecting malicious apps in official and alternative android markets,” in Proc. Symp. Netw. Distrib. Syst. Secur. (NDSS), Internet Society, 2012.

[145]

K. O. Elish, D. D. Yao, B. G. Ryder, and X. Jiang, “A static assurance analysis of android applications,” Virginia Polytechnic Institute and State University, Tech. Rep., 2013.

[146]

V. Rastogi, Y. Chen, and W. Enck, “AppsPlayground: Automatic security analysis of smartphone applications,” in Proc. 3rd ACM Conf. Data Appl. Secur. Privacy, 2013, pp. 209–220.

[147]

Y. Zhang et al., “Vetting undesirable behaviors in android apps with permission use analysis,” in Proc. ACM SIGSAC Conf. Comput. Commun. Secur., 2013, pp. 611–622.

[148]

Z. Yang, M. Yang, Y. Zhang, G. Gu, P. Ning, and X. S. Wang, “AppIntent: Analyzing sensitive data transmission in android for privacy leakage detection,” in Proc. ACM SIGSAC Conf. Comput. Commun. Secur., 2013, pp. 1043–1054.

[149]

C. Lever, M. Antonakakis, B. Reaves, P. Traynor, and W. Lee, “The core of the matter: Analyzing malicious traffic in cellular carriers,” in Proc. Symp. Netw. Distrib. Syst. Secur. (NDSS), 2013.

[150]

B. Liu, S. Nath, R. Govindan, and J. Liu, “Decaf: Detecting and characterizing ad fraud in mobile apps,” in 11th USENIX Symp. Netw. Syst. Des. Implement. (NSDI 14), Seattle, WA, USA: USENIX Association, Apr. 2014, pp. 57–70.

[151]

J. Crussell, R. Stevens, and H. Chen, “MAdFraud: Investigating ad fraud in android applications,” in Proc. 12th Annu. Int. Conf. Mobile Syst. Appl. Serv., 2014, pp. 123–134.

[152]

F. Roesner, J. Fogarty, and T. Kohno, “User interface toolkit mechanisms for securing interface elements,” in Proc. 25th Annu. ACM Symp. User Interface Softw. Technol., 2012, pp. 239–250.

[153]

F. Roesner and T. Kohno, “Securing embedded user interfaces: Android and beyond,” in Presented as part of the 22nd USENIX Security Symposium (USENIX Security 13). Washington, D.C., USA: USENIX, 2013, pp. 97–112.

[154]

D. Wigdor, C. Forlines, P. Baudisch, J. Barnwell, and C. Shen, “Lucid touch: A see-through mobile device,” in Proc. 20th Annu. ACM Symp. User Interface Softw. Technol., 2007, pp. 269–278.

[155]

M. de Sa, V. Navalpakkam, and E. F. Churchill, “Mobile advertising: Evaluating the effects of animation, user and content relevance,” in Proc. SIGCHI Conf. Hum. Factors Comput. Syst., 2013, pp. 2487–2496.

[156]

S. Egelman, A. P. Felt, and D. Wagner, “Choice architecture and smartphone privacy: There’s a price for that,” in The Economics of Information Security and Privacy. New York, NY, USA: Springer, 2013, pp. 211–236.

[157]

P. G. Kelley, M. Benisch, L. F. Cranor, and N. Sadeh, “When are users comfortable sharing locations with advertisers?” in Proc. SIGCHI Conf. Hum. Factors Comput. Syst., 2011, pp. 2449–2452.

[158]

A. P. Felt, S. Egelman, and D. Wagner, “I’ve got 99 problems, but vibration ain’t one: A survey of smartphone users’ concerns,” in Proc. 2nd ACM Workshop Secur. Privacy Smartphones Mobile Devices, 2012, pp. 33–44.

[159]

J. Jung, S. W. Shim, H. S. Jin, and H. Khang, “Factors affecting attitudes and behavioural intention towards social networking advertising: A case of facebook users in south korea,” Int. J. Advertis., pp. 1–18, 2015.

[160]

D. Cohen. (Sep. 2014). Everything You Need to Know About Facebook’s Launch of its Revamped Atlas Ad Platform Social Times [Online]. Available: http://www.adweek.com/socialtimes/atlas-launch-official/

[161]

G. Barbosa. (Jan. 2015). Top 5 Digital and Social Advertising Trends for 2015 [Online]. Available: https://www.driftrock.com/blog/top-5-digital-social-media-advertising-trends-2015

[162]

D. Tweney. (Oct. 2014). Adobe report finds massive 43 growth in online video watching [Online]. Available: http://venturebeat.com/2014/10/20/adobe-report-finds-massive-43-growth-in-online-video-watching/

[163]

H. Falaki, D. Lymberopoulos, R. Mahajan, S. Kandula, and D. Estrin, “A first look at traffic on smartphones,” in Proc. 10th ACM SIGCOMM Conf. Internet Meas., 2010, pp. 281–287.

[164]

F. Qian et al., “Periodic transfers in mobile applications: Network-wide origin, impact, and optimization,” in Proc. 21st Int. Conf. World Wide Web, 2012, pp. 51–60.

[165]

J. Huang, F. Qian, A. Gerber, Z. M. Mao, S. Sen, and O. Spatscheck, “A close examination of performance and power characteristics of 4G LTE networks,” in Proc. 10th Int. Conf. Mobile Syst. Appl. services, 2012, pp. 225–238.

Digital Library

[166]

J. Manweiler and R. Roy Choudhury, “Avoiding the rush hours: WiFi energy management via traffic isolation,” in Proc. 9th Int. Conf. Mobile Syst. Appl. Serv. (MobiSys’11), 2011, pp. 253–266 [Online]. Available: http://doi.acm.org/10.1145/1999995.2000020.

[167]

P. Mohan, S. Nath, and O. Riva, “Prefetching mobile ads: Can advertising systems afford it?” in Proc. 8th ACM Eur. Conf. Comput. Syst., 2013, pp. 267–280.

[168]

C. Nicutar, D. Niculescu, and C. Raiciu, “Using cooperation for low power low latency cellular connectivity,” in Proc. 10th ACM Int. Conf. Emerg. Netw. Exp. Technol., 2014, pp. 337–348.

[169]

F. Qian, “Web caching on smartphones: Ideal vs. reality,” in Proc. 10th Int. Conf. Mobile Syst. Appl. Serv., 2012, pp. 127–140.

[170]

D. Goldman, “The beginning of the end for adobe’s flash,” (Nov. 2011). CNNmoney Tech. [Online] Available: http://money.cnn.com/2011/11/10/technology/adobe flash/.

[171]

M. Panzarino. (2013, Mar.) Apple to Reject Any Apps That Use UDIDs [Online]. Available: http://thenextweb.com/apple/2013/03/21/after-a-year-of-warnings-apple-will-no-longer-accept-any-apps-that-//use-udids-as-of-may-1st

[172]

L. O’Reilly. (2015, Feb.) Google, Microsoft, And Amazon Are Paying Adblock Plus Huge Fees to Get Their Ads Unblocked [Online]. Available: http://www.businessinsider.com/google-microsoft-amazon-taboola-pay-adblock-plus-to-stop-blocking-//their-ads-2015-2.

[173]

C. Hoffman. (2014, Jun.) Android’s App Permissions Were Just Simplified—Now They’re Much Less Secure [Online]. Available: http://www.howtogeek.com/190863/androids-app-//permissions-were-just-simplified-now-theyre-//much-less-secure/

[174]

C. Hoffman. (2015, Oct.) How to Manage App Permissions on Android 6.0 [Online]. Available: http://www.howtogeek.com/230683/how-to-manage-app-permissions-on-android-6.0/

[175]

T. Book and D. S. Wallach, “An empirical study of mobile ad targeting,” arXiv preprint arXiv:1502.06577, 2015.

[176]

S. Nath, “Madscope: Characterizing mobile in-app targeted ads,” in Proc. 13th Annu. Int. Conf. Mobile Syst. Appl. Serv., 2015, pp. 59–73.

[177]

B. Kim et al., “AdNext: A visit-pattern-aware mobile advertising system for urban commercial complexes,” in Proc. 12th Workshop Mobile Computing Syst. Appl., 2011, pp. 7–12.

[178]

S. Oumtrakul, N. Chanuntawaree, and J. Gao, “Adtouch: A 2D-barcode mobile advertising service system,” in Security-Enriched Urban Computing and Smart Grid. New York, NY, USA: Springer, 2011, pp. 188–202.

[179]

F. N. M. Associates. (Aug. 2012). A Portrait of Today’s Smartphone User [Online]. Available: https://digitalcontentnext.org/wp-content/uploads/2002/08/MMF-OPA-Portrait-of-Smartphone-User-Aug2012.pdf

Cited By

Ogut ATuranlioglu BMetiner DLevi AYilmaz CCetin OUluagac SBalzarotti DXu W(2024)Dissecting privacy perspectives of websites around the worldProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3699060(2849-2863)Online publication date: 14-Aug-2024
https://dl.acm.org/doi/10.5555/3698900.3699060
Furini M(2023)Viewers’ behavior when exposed to overlay advertising on AVoD platformsComputers in Human Behavior10.1016/j.chb.2023.107905148:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.chb.2023.107905
Gharibshah ZZhu X(2021)User Response Prediction in Online AdvertisingACM Computing Surveys10.1145/344666254:3(1-43)Online publication date: 8-May-2021
https://dl.acm.org/doi/10.1145/3446662
Show More Cited By

Index Terms

In-Depth Survey of Digital Advertising Technologies

Index terms have been assigned to the content through auto-classification.

Recommendations

Advertising In A Digital Age
Making Ads More Relevant Innovations in Digital Advertising
SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

In the past decades, we found ourselves spending more and more time on the Internet. As we surf the Internet longer, we are exposed to digital advertisement more. Some of us got used to it and even take it for granted. And some of us sought for a remedy ...
Advertising on the Internet

Comments

Information & Contributors

Information

Published In

cover image IEEE Communications Surveys & Tutorials

IEEE Communications Surveys & Tutorials Volume 18, Issue 3

thirdquarter 2016

791 pages

ISSN:1553-877X

Issue’s Table of Contents

Copyright © 2016.

Publisher

IEEE Press

Publication History

Published: 01 July 2016

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 30 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ogut ATuranlioglu BMetiner DLevi AYilmaz CCetin OUluagac SBalzarotti DXu W(2024)Dissecting privacy perspectives of websites around the worldProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3699060(2849-2863)Online publication date: 14-Aug-2024
https://dl.acm.org/doi/10.5555/3698900.3699060
Furini M(2023)Viewers’ behavior when exposed to overlay advertising on AVoD platformsComputers in Human Behavior10.1016/j.chb.2023.107905148:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.chb.2023.107905
Gharibshah ZZhu X(2021)User Response Prediction in Online AdvertisingACM Computing Surveys10.1145/344666254:3(1-43)Online publication date: 8-May-2021
https://dl.acm.org/doi/10.1145/3446662
Xu WFan XWu TTao WXi YYang PTian C(2021)Tolerance-Oriented Wi-Fi Advertisement Scheduling: A Near Optimal Study on Accumulative User InterestsMobile Networks and Applications10.1007/s11036-021-01849-826:6(2242-2257)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1007/s11036-021-01849-8
Chua MYee GGu YLung C(2020)Threats to Online Advertising and CountermeasuresDigital Threats: Research and Practice10.1145/33741361:2(1-27)Online publication date: 29-May-2020
https://dl.acm.org/doi/10.1145/3374136
Chen GMeng WCopeland J(2019)Revisiting Mobile Advertising Threats with MAdLifeThe World Wide Web Conference10.1145/3308558.3313549(207-217)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313549

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents