Abstract
Due to the high volume of data available with social networking sites and companies, the privacy of the individuals is at a continuous risk. With the help of auxiliary data, the users can be tracked back. It becomes even more necessary to analyse the huge piles of data for research intentions. Hence, protection of privacy is a big concern. To deal with the privacy concerns, numerous privacy paradigms have been proposed to achieve an equilibrium between data utility and privacy. Anonymization of data before making it public for research is very important. Different privacy models include k-anonymity, l-diversity, t-closeness and differential privacy. This paper explores the role of quasi-identifiers and their roles for anonymization using differential privacy model. The research in this field can pave new ways for thinking before selecting quasi-identifiers for anonymization.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
J. Abawajy, M.I. Ninggal, T. Herawan, Privacy preserving social network data publication. IEEE Commun. Surv. Tutorials 18(3), 1–1 (2016)
A. Pervushin, A. Spivak, Determination of loss of information during data anonymization procedure. in 2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT), IEEE, 2016, pp. 1–5
S. Zeadally, J. Contreras-Castillo, J.A. Guerrero Ibañez, Solving vehicular ad hoc network challenges with Big Data solutions. IET, Netw. 5(4), 81–84 (2016)
J. Yang, B. Jiang, B. Li, K. Tian, Z. Lv, A fast image retrieval method designed for network or network big data. IEEE Trans. Industr. Inf. 13(5), 2350–2359 (2017)
S. Peng, G. Wang, D. Xie, Social influence analysis in social networking big data: opportunities and challenges, IEEE Netw. 12–18 (2016)
S. Yu, S. Member, Big privacy: challenges and opportunities of privacy study in the age of big data. IEEE Access 2751–2763 (2016)
J. Qian, X. Li, C. Zhang, L. Chen, Social network de-anonymization and privacy inference with knowledge graph model. IEEE Trans. Dependable Secure Comp. 5971(3), 1–14 (2017)
A. Narayanan, V. Shmatikov, Robust de-anonymization of large sparse datasets. in 2008 IEEE Symposium on Security and Privacy, IEEE, 2008, pp. 111–125
A. Abdrashitov, A. Spivak, Sensor data anonymization based on genetic algorithm clustering with L-diversity. in 2016 18th Conference on Open Innovations Association and Seminar on Information Security and Protection of Information Technology, 2016, pp. 3–8
R. Dewri, I. Ray, I. Ray, D. Whitley, K-anonymization in the presence of publisher preferences. IEEE Trans. Knowl. Data Eng. 23(11), 1678–1690 (2011)
L. Liu, S. Member, Protecting location privacy with personalized k-anonymity : architecture and algorithms. IEEE Trans. Mobile Comput. 7(1), 1–18 (2008)
J.C. Lin, Q. Liu, P. Fournier-viger, T. Hong, PTA: an efficient system for transaction database anonymization. IEEE Access 4(4), 6467–6479 (2016)
Y. Wang, J. Tian, C. Yang, Y. Zhu, Research on anonymous protection technology for big data publishing. in 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), IEEE, 2016, pp. 438–441
S. Yu, Big privacy: challenges and opportunities of privacy study in the age of big data. IEEE Access 4, 2751–2763 (2016)
J. Domingo-ferrer, D. Rebollo-monedero, J. Forne, From t-closeness-like privacy to post-randomization via information theory. IEEE Trans. Knowl. Data Eng. 22(11), 1623–1636 (2010)
I. Table, A robust privacy preserving model for data publishing. in 2015 International Conference on Computer Communication and Informatics (ICCCI), 2015, pp. 1–6
K. Mohan, P. Shrivastva, M.A. Rizvi, S. Singh, Big data privacy based on differential privacy a hope for big data. in 2014 International Conference on Computational Intelligence and Communication Networks (CICN), November, 2014, pp. 776–781
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kaur, G., Agrawal, S. (2019). Differential Privacy Framework: Impact of Quasi-identifiers on Anonymization. In: Krishna, C., Dutta, M., Kumar, R. (eds) Proceedings of 2nd International Conference on Communication, Computing and Networking. Lecture Notes in Networks and Systems, vol 46. Springer, Singapore. https://doi.org/10.1007/978-981-13-1217-5_4
Download citation
DOI: https://doi.org/10.1007/978-981-13-1217-5_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1216-8
Online ISBN: 978-981-13-1217-5
eBook Packages: EngineeringEngineering (R0)