A Privacy Preserving Framework to Protect Sensitive Data in Online Social Networks
Keywords:Data Anonymization, Social Media Privacy, Secure searchable Encryption, Personally Identifiable Information
In this day and age, Internet has become an innate part of our existence. This virtual platform brings people together, facilitating information exchange, sharing photos, posts, etc. As interaction happens without any physical presence in the medium, trust is often compromised in all these platforms operating via the Internet. Although many of these sites provide their ingrained privacy settings, they are limited and do not cater to all users’ needs. The proposed work highlights the privacy risk associated with various personally identifiable information posted in online social networks (OSN). The work is three-facet, i.e. it first identifies the type of private information which is unwittingly revealed in social media tweets. To prevent unauthorized users from accessing private data, an anonymous mechanism is put forth that securely encodes the data. The information loss incurred due to anonymization is analyzed to check how much of privacy-utility trade-off is attained. The private data is then outsourced to a more secure server that only authorized people can access. Finally, to provide effective retrieval at the server-side, the traditional searchable encryption technique is modified, considering the typo errors observed in user searching behaviours. With all its constituents mentioned above, the purported approach aims to give more fine-grained control to the user to decide who can access their data and is the correct progression towards amputating privacy violation.
E. Raad and R. Chbeir, “Privacy in Online Social Networks,” in Security and Privacy Preserving in Social Networks. Springer-Verlag Wien, 2013, pp. 3–45. [Online]. Available: https://hal.archives-ouvertes.fr/hal-00975998
J. Gehrke, E. Lui, and R. Pass, “Towards privacy for social networks: A zero-knowledge based definition of privacy,” in Theory of Cryptography, Y. Ishai, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 432–449.
Senthil Kumar N, Saravanakumar K, and Deepa K, “On privacy and security in social media – a comprehensive study,” Procedia Computer Science, vol. 78, pp. 114–119, 2016, 1st International Conference on Information Security Privacy 2015. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1877050916000211.
H. Krasnova, O. Günther, S. Spiekermann, and K. Koroleva, “Privacy concerns and identity in online social networks,” Identity in the Information Society, vol. 2, no. 1, pp. 39–63, Dec 2009. [Online]. Available: https://doi.org/10.1007/s12394-009-0019-1
A. Srivastava, “Enhancing Privacy in Online Social Networks using Data Analysis,” Birla Institute of Technology and Science, Pilani, Tech. Rep., 2015.
S. Ali, N. Islam, A. Rauf, I. U. Din, M. Guizani, and J. J. P. C. Rodrigues, “Privacy and security issues in online social networks,” Future Internet, vol. 10, no. 12, 2018. [Online]. Available: https://www.mdpi.com/1999-5903/10/12/114.
M. Fire, R. Goldschmidt, and Y. Elovici, “Online social networks: Threats and solutions,” IEEE Communications Surveys Tutorials, vol. 16, no. 4, pp. 2019–2036, 2014.
H. AbdulKader, E. ElAbd, and W. Ead, “Protecting online social networks profiles by hiding sensitive data attributes,” Procedia Computer Science, vol. 82, pp. 20–27, 2016, 4th Symposium on Data Mining Applications, SDMA2016, 30 March 2016, Riyadh, Saudi Arabia. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1877050916300187.
A. Pawar, S. Ahirrao and P. P. Churi, “Anonymization Techniques for Protecting Privacy: A Survey”,2018 IEEE Punecon, 2018, pp. 1–6, doi: 10.1109/PUNECON.2018.8745425.
K. Hasanzadeh, A. Kajosaari, D. Häggman, and M. Kyttä, “A context sensitive approach to anonymizing public participation GIS data: From development to the assessment of anonymization effects on data quality,” Computers, Environment and Urban Systems, vol. 83, p. 101513, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0198971520302465.
N. Lisin and S. Zapechnikov, “Methods and approaches for privacy-preserving machine learning,” Advanced Technologies in Robotics and Intelligent Systems Mechanisms and Machine Science, pp. 141–148, 2020.
M. Gaur, “Privacy preserving machine learning challenges and solution approach for training data in erp systems,” 2020.
M. Siddula, Y. Li, X. Cheng, Z. Tian, and Z. Cai, “Anonymization in online social networks based on enhanced equi-cardinal clustering,” IEEE Transactions on Computational Social Systems, vol. 6, no. 4, pp. 809–820, 2019.
A. Majeed, “Attribute-centric anonymization scheme for improving user privacy and utility of publishing e-health data,” Journal of King Saud University – Computer and Information Sciences, vol. 31, no. 4, pp. 426–435, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1319157817304093.
W. Ouyang and Q. Huang, “A privacy preserving algorithm for mining rare association rules by homomorphic encryption,” in 2019 6th International Conference on Systems and Informatics (ICSAI), 2019, pp. 1403–1407.
M. Dias, A. Abad, and I. Trancoso, “Exploring hashing and cryptonet based approaches for privacy-preserving speech emotion recognition,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp. 2057–2061.
W. Wei, S. Liu, W. Li, and D. Du, “Fractal intelligent privacy protection in online social network using attribute-based encryption schemes,” IEEE Transactions on Computational Social Systems, vol. 5, no. 3, pp. 736–747, 2018.
K. R. Macwan and S. J. Patel, “k-NMF anonymization in social network data publishing,” The Computer Journal, vol. 61, no. 4, pp. 601–613, 2018.
M. Yuan, L. Chen, P. S. Yu, and T. Yu, “Protecting sensitive labels in social network data anonymization,” IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 3, pp. 633–647, 2013.
H. Huang, D. Zhang, F. Xiao, K. Wang, J. Gu, and R. Wang, “Privacy-preserving approach PBCN in social network with differential privacy,” IEEE Transactions on Network and Service Management, vol. 17, no. 2, pp. 931–945, 2020.
N. Wu, F. Farokhi, D. Smith, and M. A. Kaafar, “The value of collaboration in convex machine learning with differential privacy,” 2019.
N. Holohan, S. Braghin, P. M. Aonghusa, and K. Levacher, “Diffprivlib: The ibm differential privacy library,” 2019.
A. Triastcyn and B. Faltings, “Bayesian differential privacy for machine learning,” 2020.
D. Xu, S. Yuan, and X. Wu, “Achieving differential privacy and fairness in logistic regression,” in Companion Proceedings of the 2019 World Wide Web Conference, ser. WWW ’19. New York, NY, USA: Association for Computing Machinery, 2019, pp. 594–599. [Online]. Available: https://doi.org/10.1145/3308560.3317584.
L. Chen, K. Huang, M. Manulis, and V. Sekar, “Password-authenticated searchable encryption,” International Journal of Information Security, 2020.
M. A. M. Ahsan, M. Y. Idna Bin Idris, A. W. Bin Abdul Wahab, I. Ali, N. Khan, M. A. Al-Garwi, and A. U. Rahman, “Searching on encrypted e-data using random searchable encryption (ranscrypt) scheme,” Symmetry, vol. 10, no. 5, 2018. [Online]. Available: https://www.mdpi.com/2073-8994/10/5/161.
Roesslein, J. (2020). Tweepy: Twitter for Python! URL: Https://Github.Com/Tweepy/Tweepy.
“Faker Is a Python Package That Generates Fake Data for You.” PythonRepo, https://pythonrepo.com/repo/joke2k-faker-python-testing-codebases-and-generating-test-data.
Yuanxin Li, Darina Saxunová, “A perspective on categorizing Personal and Sensitive Data and the analysis of practical protection regulations”, Procedia Computer Science, vol. 170, 2020, pp. 1110–1115, ISSN 1877-0509, Available: https://doi.org/10.1016/j.procs.2020.03.060.
Hutto, C.J. and Gilbert, E.E. (2014). “VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text.”, Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
A. Srivastava and G. Geethakumari, “Measuring privacy leaks in online social networks,” in 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2013, pp. 2095–2100.
J. Li, Q. Wang, C. Wang, N. Cao, K. Ren, and W. Lou, “Fuzzy keyword search over encrypted data in cloud computing,” in 2010 Proceedings IEEE INFOCOM, 2010, pp. 1–5.
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