Privacy preservation is a major task in cloud based applications. Many applications are built in the cloud for their economic benefits and operational convenience. The obtained information in the cloud is often seen as valuable for individuals through malicious intent. There is a lot of personal information and potentially secure data that people stored on their computers, and this information is generally transferred to the cloud. In this document, we propose an efficient index based quasi- identifier approach to ensure privacy preservation and achieve high data utility over incremental and distributed data sets. The modified Fuzzy C Means algorithm is used to construct the clusters by similarity. Anonymised data is retrieved by means of tuple partitioning in the data sets. Analysis results illustrate that the proposed method is more efficient for preserving privacy on incremental data sets than existing approaches. It is implemented in the working platform of Java.
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Pasupuleti, S.K., Ramalingam, S., Buyya, R.: An efficient and secure privacy-preserving approach for outsourced data of resource constrained mobile devices in cloud computing. J. Netw. Comput. Appl. 64, 12–22 (2016)
Chen, B.W., Rho, S., Yang, L.T.: Privacy-preserved big data analysis based on asymmetric imputation kernels and multiside similarities. Future Gener. Comput. Syst. 78, 859–866 (2016)
Hemanth Kumar, N.P., Prabhudeva, S.: Security and privacy preservation on cloud-based big data analysis (CBDA): a review. Commun. Appl. Electron. 6(1), 35–42 (2016)
Wang, H.: Privacy-preserving data sharing in cloud computing. J. Comput. Sci. Technol. 25(3), 401–414 (2010)
Lyu, C., Sun, S.F., Zhang, Y., Pande, A., Haining, L., Dawu, G.: Privacy-preserving data sharing scheme over cloud for social applications. J. Netw. Comput. Appl. 74, 44–55 (2016)
Hu, G., Xiao, D., Xiang, T., Bai, S., Zhang, Y.: A compressive sensing based privacy preserving outsourcing of image storage and identity authentication service in cloud. Inf. Sci. 387, 132–145 (2017)
Drosatosa, G., Efraimidis, P.S., Athanasiadis, I.N., Stevens, M., D’Hondt, E.: Privacy-preserving computation of participatory noise maps in the cloud. J. Syst. Softw. 92, 170–183 (2014)
Yuan, J., Tian, Y.: Practical privacy-preserving mapreduce based K-means clustering over large-scale dataset. IEEE Trans. Cloud Comput. 3(2), 1 (2017)
Hu, W., Wang, S., Chung, F.L., Liu, Y., Ying, W.: Privacy preserving and fast decision for novelty detection using support vector data description. Soft. Comput. 19(5), 1171–1186 (2014)
Herranz, J., Nin, J., Rodríguez, P., Tassa, T.: Revisiting distance-based record linkage for privacy-preserving release of statistical datasets. Data Knowl. Eng. 100, 78–93 (2015)
Patil, A.N., Phursule, R.N.: Efficient personalized privacy preservation using anonymization. Int. Res. J. Eng. Technol. (2016). https://doi.org/10.21474/IJAR01/1721
Gou, Z., Yamaguchi, S., Gupta, B.B.: Analysis of Various Security Issues and Challenges in Cloud Computing Environment: A Survey, pp. 221–247. IGI Global, Pennsylvania (2017)
Gupta, B.B.: Computer and Cyber Security: Principles, Algorithm, Applications, and Perspectives, p. 666. CRC Press, Boca Raton (2018)
Nagesh, A., Agarwal, S: Preservation of privacy using back-propagation neural networks in cloud. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 5 (2015)
Gupta, B., Agrawal, D.P., Yamaguchi, S.: Handbook of Research on Modern Cryptographic Solutions for Computer and Cyber Security. IGI Global, Pennsylvania (2016)
Stergiou, C., Psannis, K.E., Kim, B.E., Gupta, B.: Secure integration of IoT and cloud computing. Future Gener. Comput. Syst. 78, 964–975 (2018)
Zhang, X., Liu, C., Nepal, S., Chen, J.: An efficient quasi-identifier index based approach for privacy preservation over incremental data sets on cloud. J. Comput. Syst. Sci. 79(5), 542–555 (2013)
Negi, P., Mishra, A.: Enhanced CBF packet filtering method to detect DDoS attack in cloud computing environment. IJCSI Int. J. Comput. Sci. 10(2), 142–146 (2013)
Wei, L., Zhu, H., Cao, Z., Dong, X., Jia, W., Chen, Y.: Vasilakos AV, Security and privacy for storage and computation in cloud computing. J. Inf. Sci. 258, 371–386 (2014)
Gupta, B.B., Badve, O.P.: Taxonomy of DoS and DDoS attacks and desirable defense mechanism in a cloud computing environment. Neural Comput. Appl. 28(12), 3655–3682 (2016)
Gupta, B.B., Gupta, S., Chaudhary, P.: Enhancing the browser-side context-aware sanitization of suspicious HTML5 code for halting the DOM-based XSS vulnerabilities in cloud. Int. J. Cloud Appl. Comput. 7, 1–31 (2017)
Casinoa, F., Domingo-Ferrerb, J., Puigb, D.: A Solanasa (2014) k-anonymous approach to privacy preserving collaborative filtering. J. Comput. Syst. Sci. 81(6), 1000–1011 (2014)
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Sudhakar, R.V., Rao, T.C.M. Security aware index based quasi–identifier approach for privacy preservation of data sets for cloud applications. Cluster Comput 23, 2579–2589 (2020). https://doi.org/10.1007/s10586-019-03028-7
- Privacy preserving
- Cloud computing
- Modified fuzzy C means algorithm
- Anonymised data
- Quasi- identifier