Research Article
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Melting of Privacy with Machine Learning, Big Data, and Social Media

Year 2023, Volume: 7 Issue: 1, 153 - 163, 02.01.2024
https://doi.org/10.26650/acin.1231944

Abstract

Every individual has the right to keep their information private. However, there is a big question: is this possible in the digital era? While social media attracts people to share personal data, most advanced technologies are continually developing in the area of how to exploit information from this personal data. Is it possible to talk about keeping personal data private? This study aims to investigate whether it is possible both to connect to the cyber-world and remain private in the digital era, where intensive studies have been conducted to protect privacy. This study discusses: (1) the social perception of privacy, (2) the contradiction between privacy expectations and behaviors, and (3) the current state of both disclosure and protection efforts of privacy with machine learning and big data techniques. As a result of our research, it was concluded that it is almost impossible to exist in the cyber/digital world and remain private, that most users are not uncomfortable with the current situation, and that institutions and technology developers should take more responsibility in this regard.

References

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  • Jones, M. L., & Kaminski, M. E. (2021). AN AMERICAN’S GUIDE TO THE GDPR. Denver Law Review, 98(1). google scholar
  • Joshi, A. v. (2020). Machine Learning and Artificial Intelligence. Springer International Publishing. https://doi.org/10.1007/978-3-030-26622-6 google scholar
  • Kelleher, J. D., & Tierney, B. (2018). Data Science. In Data Science. https://doi.org/10.7551/mitpress/11140.001.0001 google scholar
  • Koops, B.-J. (2013). Forgetting Footprints, Shunning Shadows: A Critical Analysis of the “Right to Be Forgotten” in Big Data Practice. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1986719 google scholar
  • Kreso, I., Kapo, A., & Turulja, L. (2021). Data mining privacy preserving: Research agenda. In Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery (Vol. 11, Issue 1). https://doi.org/10.1002/widm.1392 google scholar
  • Lal Srivastava, B. M., Vauquier, N., Sahidullah, M., Bellet, A., Tommasi, M., & Vincent, E. (2020). Evaluating Voice Conversion-Based Privacy Protection against Informed Attackers. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2020-May. https:// doi.org/10.1109/ICASSP40776.2020.9053868 google scholar
  • Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META Group Research Note, 6(70). google scholar
  • Liu, B., Ding, M., Shaham, S., Rahayu, W., Farokhi, F., & Lin, Z. (2022). When Machine Learning Meets Privacy. ACM Computing Surveys, 54(2), 1-36. https://doi.org/10.1145/3436755 google scholar
  • Majeed, A., & Lee, S. (2021). Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey. IEEE Access, 9. https://doi. org/10.1109/ACCESS.2020.3045700 google scholar
  • Maleh, Y., Shojafar, M., Darwish, A., & Haqiq, A. (2019). Cybersecurity and Privacy in Cyber-Physical Systems. In Cybersecurity and Privacy in Cyber-Physical Systems. https://doi.org/10.1201/9780429263897 google scholar
  • Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., & McClosky, D. (2015). The Stanford CoreNLP Natural Language Processing Toolkit. https://doi.org/10.3115/v1/p14-5010 google scholar
  • Martinelli, F., Marulli, F., Mercaldo, F., Marrone, S., & Santone, A. (2020). Enhanced Privacy and Data Protection using Natural Language Processing and Artificial Intelligence. Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN48605.2020.9206801 google scholar
  • Marwick, A. E. (2012). The public domain: Social surveillance in everyday life. Surveillance and Society, 9(4). https://doi.org/10.24908/ss.v9i4.4342 google scholar
  • Mataric, M. J., Eriksson, J., Feil-Seifer, D. J., & Winstein, C. J. (2007). Socially assistive robotics for post-stroke rehabilitation. Journal of NeuroEngineering and Rehabilitation, 4. https://doi.org/10.1186/1743-0003-4-5 google scholar
  • Miller, A. R. (1972). Computers, Data Banks and Individual Privacy: An Overview. Colum. Hum. Rts. L. Rev. 4. google scholar
  • Mittelstadt, B. D., & Floridi, L. (2016). The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts. In Science and Engineering Ethics (Vol. 22, Issue 2). https://doi.org/10.1007/s11948-015-9652-2 google scholar
  • Niedzviecki, H. (2009). The peep diaries: How we’re learning to love watching ourselves and our neighbors. City Lights Publishers. google scholar
  • Norberg, P. A., Horne, D. R., & Horne, D. A. (2007). The privacy paradox: Personal information disclosure intentions versus behaviors. Journal of Consumer Affairs, 41(1). https://doi.org/10.1111/j.1745-6606.2006.00070.x google scholar
  • Owen, S., Anil, R., Dunning, T., & Friedman, E. (2011). Mahout in Action. In Online. google scholar
  • Patidar, H., & Umre, J. (2021). Predicting depression level using social media posts. International Journal of Research -GRANTHAALAYAH, 8(12). https:// doi.org/10.29121/granthaalayah.v8.i12.2020.1972 google scholar
  • Politou, E., Alepis, E., Virvou, M., & Patsakis, C. (2021). Privacy and Data Protection Challenges in the Distributed Era. In Learning and Analytics in Intelligent Systems. google scholar
  • Rosenberg, J. M. (1969). The Death ofPrivacy. Random House (NY). google scholar
  • Rosenthal, S., Biswas, J., & Veloso, M. (2010). An effective personal mobile robot agent through symbiotic human-robot interaction. Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, 2. google scholar
  • Schulte, P. (2018). David Vincent, Privacy. A Short History. Cambridge, Polity Press 2016. Historische Zeitschrift, 307(2). https://doi.org/10.1515/ hzhz-2018-1399 google scholar
  • Simon, H. A. (2019). Models of Bounded Rationality. In Models ofBounded Rationality. https://doi.org/10.7551/mitpress/4711.001.0001 google scholar
  • Stalder, F. (2002). Opinion. Privacy is not the antidote to surveillance. In Surveillance and Society (Vol. 1, Issue 1). https://doi.org/10.24908/ss.v1i1.3397 google scholar
  • Tay, L., Woo, S. E., Hickman, L., & Saef, R. M. (2020). Psychometric and Validity Issues in Machine Learning Approaches to Personality Assessment: A Focus on Social Media Text Mining. European Journal of Personality, 34(5). https://doi.org/10.1002/per.2290 google scholar
  • Thapa, C., Mahawaga Arachchige, P. C., Camtepe, S., & Sun, L. (2022). SplitFed: When Federated Learning Meets Split Learning. Proceedings of the google scholar
  • AAAI Conference on Artificial Intelligence, 36(8), 8485-8493. https://doi.org/10.1609/aaai.v36i8.20825 google scholar
  • T.k., B., Annavarapu, C. S. R., & Bablani, A. (2021). Machine learning algorithms for social media analysis: A survey. In Computer Science Review (Vol.40). https://doi.org/10.1016/j.cosrev.2021.100395 google scholar
  • Vepakomma, P., Gupta, O., Swedish, T., & Raskar, R. (2018). Split learning for health: Distributed deep learning without sharing raw patient data. ArXiv Preprint ArXiv:1812.00564. google scholar
  • Voigt, P., & von dem Bussche, A. (2017). The EU General Data Protection Regulation (GDPR) A Practical Guide. In The EU General Data Protection Regulation (GDPR). google scholar
  • Wang, Q., Ma, S., & Zhang, C. (2017). Predicting users’ demographic characteristics in a Chinese social media network. Electronic Library, 35(4). https:// doi.org/10.1108/EL-09-2016-0203 google scholar
  • White, T. (2012). Hadoop: The definitive guide 4th Edition. Online, 54. https://doi.org/citeulike-article-id:4882841 google scholar
  • Yu, J., Zhang, B., Kuang, Z., Lin, D., & Fan, J. (2017). IPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning. google scholar
  • IEEE Transactions on Information Forensics and Security, 12(5). https://doi.org/10.1109/TIFS.2016.2636090 google scholar
  • Yu, S. (2016). Big Privacy: Challenges and Opportunities of Privacy Study in the Age of Big Data. IEEE Access, 4. https://doi.org/10.1109/ACCESS.2016.2577036 google scholar
  • Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., & Stoica, I. (2010). Spark: Cluster computing with working sets. 2nd USENIX Workshop on Hot Topics in Cloud Computing, HotCloud 2010. google scholar
  • Zakerzadeh, H., Aggarwal, C. C., & Barker, K. (2015). Privacy-preserving big data publishing. Proceedings of the 27th International Conference on Scientific and Statistical Database Management, 1-11. https://doi.org/10.1145/2791347.2791380 google scholar
  • Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021). A survey on federated learning. Knowledge-Based Systems, 216. https://doi.org/10.1016/j.knosys.2021.106775 google scholar
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Year 2023, Volume: 7 Issue: 1, 153 - 163, 02.01.2024
https://doi.org/10.26650/acin.1231944

Abstract

References

  • Acar, G., Eubank, C., Englehardt, S., Juarez, M., Narayanan, A., & Diaz, C. (2014). The web never forgets: Persistent tracking mechanisms in the wild. Proceedings of the ACM Conference on Computer and Communications Security. https://doi.org/10.1145/2660267.2660347 google scholar
  • Acquisti, A., & Grossklags, J. (2005). Privacy and rationality in individual decision making. In IEEE Security and Privacy (Vol. 3, Issue 1). https://doi. org/10.1109/MSP.2005.22 google scholar
  • Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy and human behavior in the age of information. In Science (Vol. 347, Issue 6221). https:// doi.org/10.1126/science.aaa1465 google scholar
  • Ali, D. (2015). Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, Github, and More, by Matthew A. Russell. Journal of Information Privacy and Security, 11(2). https://doi.org/10.1080/15536548.2015.1046287 google scholar
  • Balcı, E. (2019). Overview of Intelligent Personal Assistants. Acta INFOLOGICA, 3(1). https://doi.org/10.26650/acin.571303 google scholar
  • Bauman, Z., & Lyon, D. (2013). Liquid Surveillance. In Statewide Agricultural Land Use Baseline 2015 (Vol. 1). google scholar
  • Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28. https://doi.org/10.1016/j.inffus.2015.08.005 google scholar
  • Bennett, L. (2009). Reflections on privacy, identity and consent in on-line services. Information Security Technical Report, 14(3). https://doi.org/10.1016/j. istr.2009.10.003 google scholar
  • Canbay, Y., Vural, Y., & Sagiroglu, S. (2019). Privacy Preserving Big Data Publishing. International Congress on Big Data, Deep Learning and Fighting google scholar
  • Cyber Terrorism, IBIGDELFT 2018 - Proceedings. https://doi.org/10.1109/IBIGDELFT.2018.8625358 google scholar
  • Carey, P., & Acquisti, A. (2018). Data protection: a practical guide to UK and EU law. In Economics of Information Security. google scholar
  • Chowdhary, K. R. (2020). Fundamentals of artificial intelligence. In Fundamentals of Artificial Intelligence. https://doi.org/10.1007/978-81-322-3972-7 google scholar
  • Churi, P. P., & Pawar, A. v. (2019). A systematic review on privacy preserving data publishing techniques. In Journal of Engineering Science and Technology Review (Vol. 12, Issue 6). https://doi.org/10.25103/jestr.126.03 google scholar
  • Confessore, N. (2018). Cambridge Analytica and Facebook: The Scandal and the Fallout So Far. The New York Times. google scholar
  • Correa, T., Hinsley, A. W., & de Zuniga, H. G. (2010). Who interacts on the Web?: The intersection of users’ personality and social media use. Computers in Human Behavior, 26(2). https://doi.org/10.1016/j.chb.2009.09.003 google scholar
  • Debatin, B., Lovejoy, J. P., Horn, A. K., & Hughes, B. N. (2009). Facebook and online privacy: Attitudes, behaviors, and unintended consequences. google scholar
  • Journal of Computer-Mediated Communication, 15(1). https://doi.org/10.1111/j.1083-6101.2009.01494.x google scholar
  • Deshai, N., Sekhar, B. V. D. S., & Venkataramana, S. (2019). Mllib: machine learning in apache spark. International Journal of Recent Technology and Engineering, 8(1). google scholar
  • Dwork, C. (2008). Differential privacy: A survey of results. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4978 LNCS. https://doi.org/10.1007/978-3-540-79228-4_1 google scholar
  • Fuchs, C. (2014). Social Media: A Critical Introduction. In Social Media: A Critical Introduction. https://doi.org/10.4135/9781446270066 google scholar
  • Han, B.-C. (2020). The Transparency Society. In The Transparency Society. https://doi.org/10.1515/9780804797511 google scholar
  • Hao, B., Li, L., Li, A., & Zhu, T. (2013). Predicting mental health status on social media a preliminary study on microblog. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8024 LNCS(PART 2). https://doi. org/10.1007/978-3-642-39137-8_12 google scholar
  • Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Ullah Khan, S. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98-115. https://doi.Org/10.1016/j.is.2014.07.006 google scholar
  • Hildebrandt, M. (2009). Who is Profiling Who? Invisible Visibility. In Reinventing Data Protection? https://doi.org/10.1007/978-1-4020-9498-9_14 google scholar
  • Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. In Science (Vol. 349, Issue 6245). https://doi.org/10.1126/science. aaa8685 google scholar
  • Jones, M. L., & Kaminski, M. E. (2021). AN AMERICAN’S GUIDE TO THE GDPR. Denver Law Review, 98(1). google scholar
  • Joshi, A. v. (2020). Machine Learning and Artificial Intelligence. Springer International Publishing. https://doi.org/10.1007/978-3-030-26622-6 google scholar
  • Kelleher, J. D., & Tierney, B. (2018). Data Science. In Data Science. https://doi.org/10.7551/mitpress/11140.001.0001 google scholar
  • Koops, B.-J. (2013). Forgetting Footprints, Shunning Shadows: A Critical Analysis of the “Right to Be Forgotten” in Big Data Practice. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1986719 google scholar
  • Kreso, I., Kapo, A., & Turulja, L. (2021). Data mining privacy preserving: Research agenda. In Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery (Vol. 11, Issue 1). https://doi.org/10.1002/widm.1392 google scholar
  • Lal Srivastava, B. M., Vauquier, N., Sahidullah, M., Bellet, A., Tommasi, M., & Vincent, E. (2020). Evaluating Voice Conversion-Based Privacy Protection against Informed Attackers. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2020-May. https:// doi.org/10.1109/ICASSP40776.2020.9053868 google scholar
  • Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META Group Research Note, 6(70). google scholar
  • Liu, B., Ding, M., Shaham, S., Rahayu, W., Farokhi, F., & Lin, Z. (2022). When Machine Learning Meets Privacy. ACM Computing Surveys, 54(2), 1-36. https://doi.org/10.1145/3436755 google scholar
  • Majeed, A., & Lee, S. (2021). Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey. IEEE Access, 9. https://doi. org/10.1109/ACCESS.2020.3045700 google scholar
  • Maleh, Y., Shojafar, M., Darwish, A., & Haqiq, A. (2019). Cybersecurity and Privacy in Cyber-Physical Systems. In Cybersecurity and Privacy in Cyber-Physical Systems. https://doi.org/10.1201/9780429263897 google scholar
  • Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., & McClosky, D. (2015). The Stanford CoreNLP Natural Language Processing Toolkit. https://doi.org/10.3115/v1/p14-5010 google scholar
  • Martinelli, F., Marulli, F., Mercaldo, F., Marrone, S., & Santone, A. (2020). Enhanced Privacy and Data Protection using Natural Language Processing and Artificial Intelligence. Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN48605.2020.9206801 google scholar
  • Marwick, A. E. (2012). The public domain: Social surveillance in everyday life. Surveillance and Society, 9(4). https://doi.org/10.24908/ss.v9i4.4342 google scholar
  • Mataric, M. J., Eriksson, J., Feil-Seifer, D. J., & Winstein, C. J. (2007). Socially assistive robotics for post-stroke rehabilitation. Journal of NeuroEngineering and Rehabilitation, 4. https://doi.org/10.1186/1743-0003-4-5 google scholar
  • Miller, A. R. (1972). Computers, Data Banks and Individual Privacy: An Overview. Colum. Hum. Rts. L. Rev. 4. google scholar
  • Mittelstadt, B. D., & Floridi, L. (2016). The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts. In Science and Engineering Ethics (Vol. 22, Issue 2). https://doi.org/10.1007/s11948-015-9652-2 google scholar
  • Niedzviecki, H. (2009). The peep diaries: How we’re learning to love watching ourselves and our neighbors. City Lights Publishers. google scholar
  • Norberg, P. A., Horne, D. R., & Horne, D. A. (2007). The privacy paradox: Personal information disclosure intentions versus behaviors. Journal of Consumer Affairs, 41(1). https://doi.org/10.1111/j.1745-6606.2006.00070.x google scholar
  • Owen, S., Anil, R., Dunning, T., & Friedman, E. (2011). Mahout in Action. In Online. google scholar
  • Patidar, H., & Umre, J. (2021). Predicting depression level using social media posts. International Journal of Research -GRANTHAALAYAH, 8(12). https:// doi.org/10.29121/granthaalayah.v8.i12.2020.1972 google scholar
  • Politou, E., Alepis, E., Virvou, M., & Patsakis, C. (2021). Privacy and Data Protection Challenges in the Distributed Era. In Learning and Analytics in Intelligent Systems. google scholar
  • Rosenberg, J. M. (1969). The Death ofPrivacy. Random House (NY). google scholar
  • Rosenthal, S., Biswas, J., & Veloso, M. (2010). An effective personal mobile robot agent through symbiotic human-robot interaction. Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, 2. google scholar
  • Schulte, P. (2018). David Vincent, Privacy. A Short History. Cambridge, Polity Press 2016. Historische Zeitschrift, 307(2). https://doi.org/10.1515/ hzhz-2018-1399 google scholar
  • Simon, H. A. (2019). Models of Bounded Rationality. In Models ofBounded Rationality. https://doi.org/10.7551/mitpress/4711.001.0001 google scholar
  • Stalder, F. (2002). Opinion. Privacy is not the antidote to surveillance. In Surveillance and Society (Vol. 1, Issue 1). https://doi.org/10.24908/ss.v1i1.3397 google scholar
  • Tay, L., Woo, S. E., Hickman, L., & Saef, R. M. (2020). Psychometric and Validity Issues in Machine Learning Approaches to Personality Assessment: A Focus on Social Media Text Mining. European Journal of Personality, 34(5). https://doi.org/10.1002/per.2290 google scholar
  • Thapa, C., Mahawaga Arachchige, P. C., Camtepe, S., & Sun, L. (2022). SplitFed: When Federated Learning Meets Split Learning. Proceedings of the google scholar
  • AAAI Conference on Artificial Intelligence, 36(8), 8485-8493. https://doi.org/10.1609/aaai.v36i8.20825 google scholar
  • T.k., B., Annavarapu, C. S. R., & Bablani, A. (2021). Machine learning algorithms for social media analysis: A survey. In Computer Science Review (Vol.40). https://doi.org/10.1016/j.cosrev.2021.100395 google scholar
  • Vepakomma, P., Gupta, O., Swedish, T., & Raskar, R. (2018). Split learning for health: Distributed deep learning without sharing raw patient data. ArXiv Preprint ArXiv:1812.00564. google scholar
  • Voigt, P., & von dem Bussche, A. (2017). The EU General Data Protection Regulation (GDPR) A Practical Guide. In The EU General Data Protection Regulation (GDPR). google scholar
  • Wang, Q., Ma, S., & Zhang, C. (2017). Predicting users’ demographic characteristics in a Chinese social media network. Electronic Library, 35(4). https:// doi.org/10.1108/EL-09-2016-0203 google scholar
  • White, T. (2012). Hadoop: The definitive guide 4th Edition. Online, 54. https://doi.org/citeulike-article-id:4882841 google scholar
  • Yu, J., Zhang, B., Kuang, Z., Lin, D., & Fan, J. (2017). IPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning. google scholar
  • IEEE Transactions on Information Forensics and Security, 12(5). https://doi.org/10.1109/TIFS.2016.2636090 google scholar
  • Yu, S. (2016). Big Privacy: Challenges and Opportunities of Privacy Study in the Age of Big Data. IEEE Access, 4. https://doi.org/10.1109/ACCESS.2016.2577036 google scholar
  • Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., & Stoica, I. (2010). Spark: Cluster computing with working sets. 2nd USENIX Workshop on Hot Topics in Cloud Computing, HotCloud 2010. google scholar
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There are 67 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Article
Authors

Pelin Canbay 0000-0002-8067-3365

Zübeyde Demircioğlu 0000-0002-8749-006X

Publication Date January 2, 2024
Submission Date January 10, 2023
Published in Issue Year 2023 Volume: 7 Issue: 1

Cite

APA Canbay, P., & Demircioğlu, Z. (2024). Melting of Privacy with Machine Learning, Big Data, and Social Media. Acta Infologica, 7(1), 153-163. https://doi.org/10.26650/acin.1231944
AMA Canbay P, Demircioğlu Z. Melting of Privacy with Machine Learning, Big Data, and Social Media. ACIN. January 2024;7(1):153-163. doi:10.26650/acin.1231944
Chicago Canbay, Pelin, and Zübeyde Demircioğlu. “Melting of Privacy With Machine Learning, Big Data, and Social Media”. Acta Infologica 7, no. 1 (January 2024): 153-63. https://doi.org/10.26650/acin.1231944.
EndNote Canbay P, Demircioğlu Z (January 1, 2024) Melting of Privacy with Machine Learning, Big Data, and Social Media. Acta Infologica 7 1 153–163.
IEEE P. Canbay and Z. Demircioğlu, “Melting of Privacy with Machine Learning, Big Data, and Social Media”, ACIN, vol. 7, no. 1, pp. 153–163, 2024, doi: 10.26650/acin.1231944.
ISNAD Canbay, Pelin - Demircioğlu, Zübeyde. “Melting of Privacy With Machine Learning, Big Data, and Social Media”. Acta Infologica 7/1 (January 2024), 153-163. https://doi.org/10.26650/acin.1231944.
JAMA Canbay P, Demircioğlu Z. Melting of Privacy with Machine Learning, Big Data, and Social Media. ACIN. 2024;7:153–163.
MLA Canbay, Pelin and Zübeyde Demircioğlu. “Melting of Privacy With Machine Learning, Big Data, and Social Media”. Acta Infologica, vol. 7, no. 1, 2024, pp. 153-6, doi:10.26650/acin.1231944.
Vancouver Canbay P, Demircioğlu Z. Melting of Privacy with Machine Learning, Big Data, and Social Media. ACIN. 2024;7(1):153-6.