ANALYSIS OF DATA STREAM ANONYMIZATION DISTRIBUTION AS LINKED TO PRIVACY PRESERVING

Ruchika Chakravarti

Vol. 6, Issue 1, Jan-Dec 2020

Page Number: 133 - 139

Abstract:

Sustainable stream handling analyses have acquired prevalence as of late. Stream control is an approach to looking at and changing constant information streams. Missing qualities are pervasive in certifiable information streams, which shields information stream protection testing. Then again, most protection safeguarding techniques need not consider missing qualities when created. They can anonymize information in a specific report. Be that as it may, this outcome is information misfortune. This exploration proposes an extraordinary equal conveyed approach for safeguarding protection while utilizing fragmented information streams. This strategy utilizes a computational creation framework to constantly anonymize information streams, utilizing bunching to build each tuple. It bunches information in fractional and complete structures involving variables and exhibits aspects as comparability measurements. A speculation approach given more than matches is utilized to forestall the contamination of values and exceptions. The analyses utilized genuine information to contrast current frameworks and fluctuated settings. This examination will cover a few anonymization instruments and their benefits. There are likewise disadvantages. Finally, we will investigate the fate of persistent information anonymization research.

References

• L. Yang, X. Chen, Y. Luo, X. Lan, and W. Wang, “IDEA: A utility-enhanced approach to incomplete data stream anonymization,” Tsinghua Sci. Technol., vol. 27, no. 1, pp. 127–140, 2022, Doi: 10.26599/TST.2020.9010031.
• S. De Capitani Di Vimercati et al., “Artifact: Scalable Distributed Data Anonymization,” 2021 IEEE Int. Conf. Pervasive Comput. Commun. Work. other Affil. Events, PerCom Work. 2021, pp. 450–451, 2021, doi: 10.1109/PerComWorkshops51409.2021.9431059.
• Pelin Canbay and Hayri Sever, “The Effect of Clustering on Data Privacy“ 2015 IEEE International Conference on. IEEE 2015.
• Mohamed Nassar, Abdelkarim Erradi, Qutaibah M. Malluhi, “Paillier’s Encryption: Implementation and Cloud Applications” KINDI Center for Computing Research Qatar University Doha, Qatar.
• Mohammad-Reza Zare-Mirakabad, Fatemeh Kaveh-Yazdy, Mohammad Tahmasebi, “Privacy Preservation by k-anonymizing Ngrams of Time Series” Yazd University, Iran, Dalian University of Technology, Dalian.
• Tsubasa Takahashi , Koji Sobataka , Takao Takenouchi , Yuki Toyoda , Takuya Mori and Takahide Kohroy “Top-Down Itemset Recording for Releasing Private Complex Data” Cloud System Research Laboratories, NEC Corporation, Kawasaki, Kanagawa Japan, Jichi Medical University Hospital, Shimotsuke, Tochigi Japan. IEEE 2013.
• Ninghui Li, Tiancheng Li, Suresh Venkata Subramanian, “T-Closeness: Privacy Beyond k-Anonymity and lDiversity” Department of Computer Science, Purdue University, AT&T Labs – Research. IEEE 2007.
• Nirav. U.Patel, Vaishali.R.Patel, “Anonymization of Social Networks for Reducing Communication Complexity and Information Loss by Sequential Clustering”, 2015.
• Mahesh, R., A New Method for Preserving Privacy in Data Publishing Against Attribute and Identity Disclosure Risk (2013). International Journal on Cryptography and Information Security (IJCIS), Vol.3, No. 2, June 2013, Available at SSRN: https://ssrn.com/abstract=3685781
• R. B. Ghate and R. Ingle, 'Clustering based Anonymization for privacy preservation,' in Pervasive Computing (ICPC), 2015 International Conference on, 2015.
• M. B. Malik, M. A. Ghazi, and R. Ali, ‘‘Privacy preserving data mining techniques: Current scenario and future prospects,’’inProc.3rdInt.Conf. Comput. Commun. Technol. (ICCCT), Nov. 2012, pp. 26–32.
• M.-J. Choi, H.-S. Kim and Y.-S. Moon. 'Publishing time-series data under preservation of privacy and distance orders'. International Journal of Innovative Computing, Information and Control (IJICIC), Vol. 8, pp. 3619- 3638, 2012.
• X. Xiao and Y. Tao. Personalized privacy preservation. In Proceedings of ACM Conference on Management of Data (SIGMOD’06), pages 229–240, June 2006.
• C. C. Aggarwal and S. Y. Philip, A general survey of privacy-preserving data mining models and algorithms: Springer, 2008. 21. Olga Gkountouna, A Survey on Privacy Preservation Methods, June -2011.
• Pierangela Samarati and Latanya Sweeney, Protecting Privacy When Disclosing Information: K-Anonymity and its Enforcement through Generalization and Suppression.
• Freny Presswala, Amit Thakkar and Nirav Bhatt, Survey on Anonymization on in Privacy Preserving Data Mining, International Journal of Innovative and Emerging Research in Engineering (IJIERE), 2015.

Back Download