Machine Learning and Data Privacy in Digital Advertising

Öz

Digital advertising provides great advantages such as lower advertising costs, fast and reliable feedbacks from customers, increased efficiency, and the ability to create detailed databases of customers, which make it increasingly more important for companies. Production of contents is mainly based on intuition and experience in conventional advertising, while it is based on data in digital advertising. This makes it possible to offer targeted advertisements that are customized according to the digital trails of consumers. Targeted advertising has become the focus of digital advertising, and methods that have been developed in this field open new horizons both for companies and researchers. To provide targeted advertisements for digital advertising, bidding machines or pricing engines that offer customized prices and promotions are typically generated by means of a machine learning algorithm. Machine learning provides companies with more power to control advertisements; but the most important issue of debate is the customization of advertisements and therefore the possibility that data privacy is compromised. This paper discusses the issue with a holistic approach by focusing on the concerns of data privacy in addition to the benefits of targeted advertisements and machine learning algorithms for businesses. This paper also discusses the steps that would prevent consumers from not proceeding with a purchase due to concerns about data privacy, while maintaining the high level of profitability gained thanks to targeted advertisements. As a result, the importance of using consumer data in digital advertising was emphasized. However, privacy should be configured within the limits of consumer privacy by making personal data privacy settings with machine learning algorithms. Thus, it will be possible for companies both to protect their profitability and prevent consumer losses due to data privacy.

Anahtar Kelimeler

• Machine Learning
• Data Privacy
• Digital Advertising
• Targeted Advertising
• Artificial Intelligence

Dijital Reklamcılıkta Makine Öğrenmesi ve Veri Gizliliği

Öz

Dijital reklamcılık düşük reklam maliyetleri, hızlı ve etkili tüketici geri bildirimi, artan verimlilik ve ayrıntılı müşteri tabanı oluşturma avantajlarından dolayı şirketler için giderek daha önemli hale gelmektedir. Geleneksel reklamcılıkta daha çok sezgiye ve tecrübeye dayanan içerik üretme, dijital reklamcılıkta veriye dayalıdır. Böylece tüketicilerin dijital izlerine göre kişiselleştirilmiş hedef reklamlar sunulmaktadır. Hedef reklamcılık, dijital reklamcılığın odağına yerleşirken, bu alanda geliştirilen yöntemler hem şirketler hem de araştırmacılar için yeni ufuklar açmaktadır. Dijital reklamcılıkta hedefli reklamların sunulmasında teklif verme makineleri veya kişiye özel fiyat ve promosyon sunan fiyatlandırma motoru, genel olarak gelişmiş bir makine öğrenmesi algoritmasıyla gerçekleştirilmektedir. Makine öğrenmesi, şirketlere reklam üzerinde daha fazla kontrol gücü verirken, en önemli tartışma konusu ise reklamların kişiselleştirilmesi ve bunun sonucu olarak veri gizliliği ihlallerinin yaşanabilmesidir. Bu makale, makine öğrenmesi algoritmaları ile hedef reklamcılığın işletmelere sağladığı faydalar yanında, veri gizliliği endişelerine de odaklanarak konuyu bütüncül bir yaklaşımla ele almaktadır. Makalede hedef reklamcılığın getirdiği yüksek karlılığı korurken, tüketicilerin veri gizliliği endişesiyle satın alma davranışından vazgeçmelerini engelleyecek adımların neler olduğu tartışılmıştır. Sonuç olarak tüketici verilerinin dijital reklamcılıkta kullanılmasının önemi ortaya çıkmıştır. Bununla birlikte makine öğrenmesi algoritmaları ile kişiye özgü veri gizlilik ayarlarının yapılarak mahremiyetin, tüketicinin gizlilik sınırları çerçevesinde yapılandırılması gerektiği vurgulanmaktadır. Böylece şirketlerin hem kârlılığı koruması hem de veri gizliliği nedeniyle tüketici kayıplarının önüne geçmesi mümkün olacaktır.

Anahtar Kelimeler

• Makine Öğrenmesi
• Veri Gizliliği
• Dijital Reklamcılık
• Hedef Reklamcılık
• Yapay Zekâ

Kaynakça

1. Acquisti, A. ve Spiekermann, S. (2011). Do Interruptions Pay Off? Effects Of Interruptive Ads On Consumers’ Willingness to Pay. Journal of Interactive Marketing, 25(4), 226-240. https://doi.org/10.1016/j.intmar.2011.04.003
2. Almuhimedi, H., Schaub, F., Sadeh, N., Adjerid, I., Acquisti, A., Gluck, J., ... ve Agarwal, Y. (2015, April). Your Location Has Been Shared 5,398 Times! A Field Study On Mobile App Privacy Nudging. In Proceedings of the 33rd annual ACM conference on human factors in computing systems (ss.787-796).
3. Alzubi, O. A., Alzubi, J. A., Alweshah, M., Qiqieh, I., Al-Shami, S., ve Ramachandran, M. (2020). An Optimal Pruning Algorithm Of Classifier Ensembles: Dynamic Programming Approach. Neural Computing and Applications, 32(20), 16091-16107. https://doi.org/10.1007/s00521-020-04761-6
4. Avila Clemenshia, P. ve Vijaya, M. S. (2016). Click Through Rate Prediction For Display Advertisement. International Journal of Computer Applications (975-8887), 1(136), 18-24
5. Bansal, G., Zahedi, F.M. ve Gefen, D. (2010). The Impact Of Personal Dispositions On Information Sensitivity, Privacy Concern And Trust In Disclosing Health Information Online. Decision Support Systems, 49(2), 138-150. https://doi.org/10.1016/j.dss.2010.01.010
6. Bari, L., & O’Neill, D. P. (2019). Rethinking Patient Data Privacy in The Era Of Digital Health. Health Affairs, 12. https://www.healthaffairs.org/do/10.1377/forefront.20191210.216658
7. Baruh, L., Secinti, E. ve Cemalcilar, Z. (2017). Online Privacy Concerns And Privacy Management: A Meta-Analytical Review. Journal of Communication, 67(1), 26-53. https://doi.org/10.1111/jcom.12276
8. Bélanger, F. ve Crossler, R. E. (2011). Privacy In The Digital Age: A Review Of Information Privacy Research In Information Systems. MIS Quarterly, 1017-1041. https://doi.org/10.2307/41409971

9. Bélanger, F., Hiller, J. S. ve Smith, W. J. (2002). Trustworthiness In Electronic Commerce: The Role Of Privacy, Security, And Site Attributes. The Journal Of Strategic Information Systems, 11(3-4), 245-270. https://doi.org/10.1016/S0963-8687(02)00018-5
10. Bergemann, D. ve Bonatti, A. (2011). Targeting In Advertising Markets: Implications For Offline Versus Online Media. The RAND Journal of Economics, 42(3), 417-443. https://doi.org/10.1111/j.1756-2171.2011.00143.x
11. Bleier, A., Goldfarb, A. ve Tucker, C. (2020). Consumer Privacy And The Future Of Data-Based Innovation And Marketing. International Journal of Research in Marketing, 37(3), 466-480. https://doi.org/10.1016/j.ijresmar.2020.03.006
12. Breiman L. (2001). Random Forests, Machine Learning, 45 (1), 5-32.
13. Campbell, J., Goldfarb, A. ve Tucker, C. (2015). Privacy Regulation And Market Structure. Journal of Economics & Management Strategy, 24(1), 47-73. https://doi.org/10.1111/jems.12079
14. Chapelle, O., Manavoglu, E. ve Rosales, R. (2014). Simple And Scalable Response Prediction For Display Advertising. ACM Transactions on Intelligent Systems and Technology (TIST), 5(4), 1-34. https://doi.org/10.1145/2532128
15. Chen, S. ve Li, J. (2009, May). Factors Influencing The Consumers' Willingness To Buy In E-Commerce. In 2009 International Conference on E-Business and Information System Security (pp. 1-8). IEEE. https://doi.org/10.1109/EBISS.2009.5137979
16. Choi, J. A., & Lim, K. (2020). Identifying Machine Learning Techniques For Classification Of Target Advertising. ICT Express, 6(3), 175-180. https://doi.org/10.1016/j.icte.2020.04.012
17. Clarke, R. (1999). Internet Privacy Concerns Confirm The Case For Intervention. Communications of the ACM, 42(2), 60-67. https://doi.org/10.1145/293411.293475
18. Culnan, M. J. ve Armstrong, P. K. (1999). Information Privacy Concerns, Procedural Fairness, And Impersonal Trust: An Empirical Investigation. Organization Science, 10(1), 104-115. https://doi.org/10.1287/orsc.10.1.104
19. Dinev, T. ve Hart, P. (2006). An Extended Privacy Calculus Model For E-Commerce Transactions. Information Systems Research, 17(1), 61-80.
20. Drennan, J., Sullivan, G. ve Previte, J. (2006). Privacy, Risk Perception, And Expert Online Behavior: An Exploratory Study Of Household End Users. Journal of Organizational and End User Computing (JOEUC), 18(1), 1-22. https://doi.org/10.4018/joeuc.2006010101
21. Eastlick, M. A., Lotz, S. L. ve Warrington, P. (2006). Understanding Online B-To-C Relationships: An Integrated Model Of Privacy Concerns, Trust, And Commitment. Journal Of Business Research, 59(8), 877-886. https://doi.org/10.1016/j.jbusres.2006.02.006
22. Ekinci, E., Omurca, S. İ., Kırık, E. ve Taşçı, Ş. (2020). Tıp Veri Kümesi İçin Gizli Dirichlet Ayrımı. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, 22 (64), 67-80. https://doi.org/10.21205/deufmd.2020226408
23. Goldfarb, A. ve Tucker, C. (2011). Online Display Advertising: Targeting And Obtrusiveness. Marketing Science, 30(3), 389-404. https://doi.org/10.1287/mksc.1100.0583
24. Gomez, J., Pinnick, T. ve Soltani, A. (2009). Knowprivacy: The Current State Of Web Privacy, Data Collection, And Information Sharing. Berkeley, CA: UC Berkeley School of Information. https://www.ischool.berkeley.edu/projects/2009/knowprivacy
25. Gülpınar Demirci, V. ve Altaş, D. (2020). Yapay sinir ağları. D. Altaş ve İ. E. Yıldırım (Ed.), Uygulamalı Çok Değişkenli İstatistik Teknikler İçinde (s.167-188). Eskişehir: Seçkin Yayınevi.
26. Gülpınar Demirci, V. ve Kaplan, B. (2020). Veri madenciliği ve pazarlama. C. Söylemez ve A. Kayabaşı (Ed.) Dijital Pazarlama: Güncel Konular içinde (s.253-282). Bursa: Ekin Yayınevi.
27. Hsu, C. W. ve Lin, C. J. (2002). A Comparison Of Methods For Multiclass Support Vector Machines. IEEE Transactions On Neural Networks, 13(2), 415-425. https://doi.org/10.1109/72.991427
28. Hummel, P., Braun, M. ve Dabrock, P. (2021). Own Data? Ethical Reflections On Data Ownership. Philosophy & Technology, 34(3), 545-572. https://doi.org/10.1007/s13347-020-00404-9
29. Johnson, G. A., Shriver, S. K. ve Du, S. (2020). Consumer Privacy Choice In Online Advertising: Who Opts Out And At What Cost To Industry?. Marketing Science, 39(1), 33-51. https://doi.org/10.1287/mksc.2019.1198
30. Khan, K., Rehman, S. U., Aziz, K., Fong, S. ve Sarasvady, S. (2014, February). DBSCAN: Past, present and future. In The fifth international conference on the applications of digital information and web technologies (ICADIWT 2014) (ss.232-238). IEEE. https://doi.org/10.1109/ICADIWT.2014.6814687
31. Kuppusamy, K. S. (2018). Machine Learning Based Heterogeneous Web Advertisements Detection Using A Diverse Feature Set. Future Generation Computer Systems, 89, 68-77. https://doi.org/10.1016/j.future.2018.06.028
32. Lee, H. ve Cho, C. H., (2020) Digital Advertising: Present and Future Prospects. International Journal of Advertising, 39(3), 332-341. https://doi.org/10.1080/02650487.2019.1642015
33. Liao, Y., Vitak, J., Kumar, P., Zimmer, M. ve Kritikos, K. (2019, March). Understanding the role of privacy and trust in intelligent personal assistant adoption. In International Conference on Information (ss. 102-113). Springer, Cham.
34. Liu, B., Ding, M., Shaham, S., Rahayu, W., Farokhi, F. ve Lin, Z. (2021). When Machine Learning Meets Privacy. ACM Computing Surveys, 54(2), 1–36. https://doi.org/10.1145/3436755
35. Ma, L. ve Sun, B. (2020). Machine Learning And AI In Marketing – Connecting Computing Power To Human Insights. International Journal of Research in Marketing, 37(3), 481-504. https://doi.org/10.1016/j.ijresmar.2020.04.005
36. Malhotra, N. K., Kim, S. S. ve Agarwal, J. (2004). Internet Users’ Information Privacy Concerns (IUIPC): The Construct, the Scale, and a Causal Model. Information Systems Research, 15(4), 336–355. https://doi.org/10.1287/isre.1040.0032
37. Marini, F. ve Amigo, J. M. (2020). Unsupervised Exploration Of Hyperspectral And Multispectral Images. In Data Handling in Science and Technology, 32, 93-114. https://doi.org/10.1016/B978-0-444-63977-6.00006-7
38. Metheny, M. (2017). Security and privacy in public cloud computing. Federal Cloud Computing, Federal Cloud Computing: The Definitive Guide for Cloud Service Providers içinde, Second Edition, Elsevier Inc, Syngress, ss. 79–115.
39. Miller, A. R. ve Tucker, C. (2009). Privacy Protection And Technology Diffusion: The Case Of Electronic Medical Records. Management Science, 55(7), 1077-1093. https://doi.org/10.1287/mnsc.1090.1014
40. Mitchell, T. (1997). Machine Learning. New York: McGraw Hill.
41. NIST Special Publication (2013). Security And Privacy Controls For Federal Information Systems And Organizations, Revision 5, 800 (53), 8-13. https://doi.org/10.6028/NIST.SP.800-53r5
42. Onan, A., Korukoğlu, S. ve Bulut, H. (2016). Ensemble Of Keyword Extraction Methods And Classifiers İn Text Classification. Expert Systems with Applications, 57, 232–247. https://doi.org/10.1016/j.eswa.2016.03.045
43. Pan, S. J. ve Yang, Q. (2009). A Survey On Transfer Learning. IEEE Transactions On Knowledge And Data Engineering, 22(10), 1345-1359. https://doi.org/10.1109/TKDE.2009.191
44. Pavlou, P. A. (2011). State Of The Information Privacy Literature: Where Are We Now And Where Should We Go? MIS Quarterly, 35(4), 977–988. https://doi.org/10.2307/41409969
45. Perlich, C., Dalessandro, B., Raeder, T., Stitelman, O. ve Provost, F. (2014). Machine Learning For Targeted Display Advertising: Transfer Learning In Action. Machine Learning, 95(1), 103-127. https://doi.org/10.1007/s10994-013-5375-2
46. Prainsack, B. (2019). Logged Out: Ownership, Exclusion And Public Value In The Digital Data And Information Commons. Big Data & Society, 6(1), 1-15. https://doi.org/10.1177/2053951719829773
47. Ren, K., Zhang, W., Chang, K., Rong, Y., Yu, Y. ve Wang, J. (2017). Bidding Machine: Learning To Bid For Directly Optimizing Profits In Display Advertising. IEEE Transactions on Knowledge and Data Engineering, 30(4), 645-659. https://doi.org/10.1109/TKDE.2017.2775228
48. Shah, N., Engineer, S., Bhagat, N., Chauhan, H. ve Shah, M. (2020). Research Trends on the Usage of Machine Learning and Artificial Intelligence in Advertising. Augmented Human Research, 5(1), 1-19. https://doi.org/10.1007/s41133-020-00038-8
49. Shanahan, J. G. ve Kurra, G. (2011). Digital Advertising: An Information Scientist’s Perspective. In Advanced Topics in Information Retrieval (s. 209-237). Springer, Berlin, Heidelberg.
50. Sharma, A., Kulkarni, S. V., Kalbande, D. ve Dholay, S. (2019). Cost Optimized Hybrid System İn Digital Advertising Using Machine Learning. Int J Innov Technol Explor Eng, 8(8), 934-939.
51. Shiller, B., Waldfogel, J. ve Ryan, J. (2018). The Effect Of Ad Blocking On Website Traffic And Quality. The RAND Journal of Economics, 49(1), 43-63. https://doi.org/10.1111/1756-2171.12218
52. Smith, H. J., Dinev, T. ve Xu, H. (2011). Information Privacy Research: An Interdisciplinary Review. MIS Quarterly, 35(4), 989-1015. https://doi.org/10.2307/41409970
53. Sutton, R. S. ve Barto, A. G. (2018). Reinforcement Learning: An İntroduction. MIT Press.
54. Şahin, Ü. ve Yücesoy, V. (2019). Çok Kollu Haydutlar İle Dinamik Ambulans Konumlandırma. In 27th Signal Processing and Communications Applications Conference (SIU), 2019 (ss. 1-4). IEEE.
55. Tesfay, W. B., Hofmann, P., Nakamura, T., Kiyomoto, S. ve Serna, J. (2018, April). I Read But Don't Agree: Privacy Policy Benchmarking Using Machine Learning And The EU GDPR. In Companion Proceedings of the The Web Conference 2018 (ss. 163-166).
56. Tucker, C.E. (2015). Privacy and the internet. In Handbook of Media Economics (Eds. Simon P. Anderson, Joel Waldfogel, David Strömberg), Volume 2, Elsevier B.V. pp. 541-562. https://doi.org/10.1016/B978-0-444-63685-0.00011-5
57. Vapnik, V. (1995). The Nature Of Statistical Learning Theory. Newyork: Springer-Verlag.
58. Vermorel, J. ve Mohri, M. (2005, October). Multi-armed bandit algorithms and empirical evaluation. In European conference on machine learning (ss. 437-448). Springer, Berlin, Heidelberg.
59. Vural, Y. (2018). Veri Mahremiyeti: Saldırılar, Korunma Ve Yeni Bir Çözüm Önerisi. Uluslararası Bilgi Güvenliği Mühendisliği Dergisi, 4(2), 21-34. https://doi.org/10.18640/ubgmd.517767
60. Watkins, C. J. ve Dayan, P. (1992). Q-Learning. Machine Learning, 8(3), 279-292.
61. Weber, R. H. (2010). Internet of Things–New Security And Privacy Challenges. Computer Law & Security Review, 26(1), 23-30. https://doi.org/10.1016/j.clsr.2009.11.008
62. Wen, T. J., Chuan, C. H., Yang, J., & Tsai, W. S. (2022). Predicting Advertising Persuasiveness: A Decision Tree Method for Understanding Emotional (In) Congruence of Ad Placement on YouTube. Journal of Current Issues & Research in Advertising, 43(2), 200-218. https://doi.org/10.1080/10641734.2021.1963356
63. Westin, A.F. (1967). Privacy And Freedom. New York: Atheneum.
64. Wieringa, J., Kannan, P. K., Ma, X., Reutterer, T., Risselada, H. ve Skiera, B. (2021). Data Analytics In A Privacy-Concerned World. Journal of Business Research, 122, 915-925. https://doi.org/10.1016/j.jbusres.2019.05.005
65. Wu, P. F., Vitak, J. ve Zimmer, M. T. (2019). A Contextual Approach To Information Privacy Research. Journal of the Association for Information Science and Technology, 1-6. https://doi.org/10.1002/asi.24232
66. Zins, C. (2007). Conceptual Approaches For Defining Data, Information, And Knowledge. Journal Of The American Society For Information Science And Technology, 58(4), 479-493. https://doi.org/10.1002/asi.20508