A Model to Publish Online Social Networks Data with Privacy Preserving

Kosari Langari, Rohulla; Sardar, Soheila; Amin Mousavi, Seyed Abdollah; Radfar, Reza

doi:10.22054/ims.2019.10377

Document Type : Research Paper

Authors

¹ Ph.D. Candidate, Information Technology Management, Faculty of Management, Tehran North Branch, Islamic Azad University, Tehran.

² Faculty Member, Department of Industrial Management, Faculty of Management, Tehran North Branch, Islamic Azad University, Tehran.(Corresponding Author: s_sardar@iau-tnb.ac.ir)

³ Faculty Member, Department of Information Technology Management, Faculty of Management, Science and Research Branch, Islamic Azad University, Tehran

⁴ Faculty Member, Department of Technology Management, Faculty of Management, Science and Research Branch, Islamic Azad University, Tehran.

https://doi.org/10.22054/ims.2019.10377

Abstract

Nowadays the growth in the use of social networks among different classes of world community is increasingly undeniable. Social networks database include Rich and valuable resources whose release and analysis with the purpose of marketing, publicity, National Security, Health and etc. can benefit researchers of public and private institutions. But respect the privacy of the entities whose information is available to data miner analysis is essential as a legal protocol. In this Paper, through qualitative methodology Meta synthesis, all related dimensions, indicators and codes were identified and then the importance and priority of each of the factors was determined. Subsequently, the improved model of anonymity was presented by an optimizing firefly algorithm and fuzzy clustering. The result of simulation and assessment of the proposed model on the data of four social networks such as Facebook, YouTube, Twitter and Google+ depicts that privacy preserving of data with the lowest distortion ratio and the more usefulness of data.

Keywords

References

سرگلزایی، احسان؛ و عبدالهی ازگمی، محمد. (آذرماه ۱۳۹۲). ارائه الگوریتمی حریصانه برای حفظ حریم خصوصی دادههای منتشرشده شبکه‌های اجتماعی. اولین همایش ملی کاربرد سیستم‌های هوشمند (محاسبات نرم) در علوم و صنایع، دانشگاه آزاد اسلامی واحد قوچان.

Afshari, M. H., Dehkordi, M. N., & Akbari, M. (2016). Association rule hiding using cuckoo optimization algorithm, Expert Systems with Applications, 64: 340-351.

Akshaya, T., Amrit, P. (2016). Data mining with big data and Privacy preservation, International Journalof Advanced Research in Computer and Communication Engineering Vol. 5(4), 1121-1124.

Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and new challenges, 28(3), 45-59.

Bingchun, L., & Guohua, L. (2011). The classification of k-anonymity data, seventh international conference on computational intelligence and security, pp.1374-1378.

Chen, R., Fung, B. C., Philip, S. Y., & Desai, B. C. (2014). Correlated network data publication via differential privacy, The VLDB Journal, 23(4), 653-676.

ElBarawy, Y., Mohamed, M., & Ghali, N. I. (2014). Improving social network community detection using DBSCAN algorithm, World Symposium Computer Application & Research (WSCAR), pp. 1-6.

Ferrag, M. A., Maglaras, L., & Ahmim, A. (2017). Privacy-preserving schemes for ad hoc social networks: A survey. IEEE Communications Surveys & Tutorials, 19(4), pp. 3015-3045.

Hartung, S., Hoffmann, C., & Nichterlein, A. (2014). Improved upper and lower bound heuristics for degree anonymization in social networks, International Symposium on Experimental Algorithm, pp. 376-387.

Honda, K., Kawano, A., Notsu, A., & Ichihashi, H. (2012). A fuzzy variant of k-member clustering for collaborative filtering with data anonymization, In IEEE Conference on Fuzzy Systems, pp. 1-6.

Li, M., Liu, Z., & Dong, K. (2016). Privacy preservation in social network against Public neighborhood attacks, In Trustcom/BigDataSE/I SPA, 2016 IEEE, pp. 1575-1580.

Li, N., Li, T., & Venkatasubramanian, S. (2007). T-Closeness: Privacy Beyond k-anonymity and ℓ-Diversity, in: Proceedings of ICDE, pp.106-115.

Liu, P., Cui, L., & Li, X. (2014). A hybrid algorithm for privacy preserving social network publication, In Advanced Data Mining and Applications, pp. 267-278.

Machanavajjhala, A., Gehrke, J. & Kifer, D. (2006). L-diversity: Privacy beyond k-anonymity, in: Proceedings of ICDE, pp. 24.

Mandapati, S., Bhogapathi, R. B., Rao, M. C. S., & Vjiet, V. (2013). Swarm optimization algorithm for privacy preserving in data mining, Int. J. Comput. Sci. Issues, 10(2).

Rahimi, M., Bateni, M., & Mohammadinejad, H. (2015). Extended k-anonymity model for privacy preserving on micro data, IJ Comput Netw INF Secur, 7(12), 42-51.

Sandelowski, M., and Barroso, J, (2007). Handbook for synthesizing qualitative research, New York, NY: Springer.

Schlegel, R., Chow, C. Y., Huang, Q., & Wong, D. S. (2017). Privacy-preserving location sharing services for social networks, IEEE Transactions on Services Computing, Vol. 10(3), 811-825.

Sihag, V. K. (2012). A clustering approach for structural k-anonymity in social networks using genetic algorithm, Proceedings of the CUBE International Information Technology Conference, pp. 701-706.

Susan, V. S., & Christopher, T. (2016). Privacy preserving data mining using multiple objective optimization, ICTACT Journal on Soft Computing, 7(4), 1366-1371.

Sweeney, L. (2002). Achieving k-anonymity privacy protection using generalization & suppression, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 571-588.

Tan, Y., Takagi, H., & Shi, Y. (Eds.). (2017). Data mining and Big data: Second International Conference”, DMBD 2017, Fukuoka, Japan, Proceedings, 10387: 9-21

Tai, C., Yu, P., & Chen, M. (2010). K-support anonymity based on pseudo taxonomy for outsourcings of frequent item set mining, in: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 16(2), 473-482.

Tiwari, A., & Choudhary, M. (2017). A review on k-Anonymization techniques, Scholars Journal of Engineering and Technology (SJET), 5(6), 238-245.

Torra, V., & Navarro-Arribas, G. (2015). Data privacy: A survey of results, in advanced research in data privacy, springer international publishing, 567(2), pp. 27-37.

Torra, G., & Erola, A., & Roca, J. (2012). User k-anonymity for privacy preserving data mining of query logs, Information Processing and Management,48(3), 476–487.

Truta, T. M., & Vinay, B. (2006). Privacy protection: p-sensitive k-anonymity property. In 2nd International Workshop on Privacy Data Management PDM 2006, p. 94, Berlin Heidelberg, 2006. IEEE Computer Society.

Waal, T., & Willenborg, L. (1999). Information loss through global recoding and local suppression, 14: 17-20.

Yang, C. C. (2011). Preserving privacy in social network integration with τ-tolerance, In Intelligence and Security Informatics (ISI), IEEE International Conference on pp. 198-200.

Business Intelligence Management Studies

A Model to Publish Online Social Networks Data with Privacy Preserving

References

References

Volume 8, Issue 29 - Serial Number 29
November 2019
Pages 87-112

A Model to Publish Online Social Networks Data with Privacy Preserving

References

References

Volume 8, Issue 29 - Serial Number 29November 2019Pages 87-112

Volume 8, Issue 29 - Serial Number 29
November 2019
Pages 87-112