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A Novel Study of Different Privacy Frameworks Metrics and Patterns

  • Sukumar Rajendran
  • J. PrabhuEmail author
Conference paper
  • 12 Downloads
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 127)

Abstract

Learnability has impacted data privacy and security exposing two sides of a coin. A breach in security eventually leads to loss of privacy and vice versa. Evolution of technologies has put forth new platforms simplifying data derivation and assimilation providing information on the go. Even though different policies and metrics are in place, the objective varies along with the factors determined by technological advancement. This paper describes existing privacy metrics and patterns while providing an overall view of different mathematical framework privacy preserving. Furthermore, maintaining trust and utility becomes a challenge in preserving privacy and security as different techniques and technologies for assimilation of information are readily available without any restraints.

Keywords

Privacy Trust Utility Differential privacy 

References

  1. 1.
    Ahmad, A., Mukkamala, R.: A novel information privacy metric. In: Information Technology–New Generations, pp. 221–226. Springer, Berlin (2018)Google Scholar
  2. 2.
    Avent, B., Korolova, A., Zeber, D., Hovden, T., Livshits, B.: \(\{\)BLENDER\(\}\): Enabling local search with a hybrid differential privacy model. In: 26th \(\{\)USENIX\(\}\) Security Symposium (\(\{\)USENIX\(\}\) Security 17), pp. 747–764 (2017)Google Scholar
  3. 3.
    Bebensee, B.: Local differential privacy: a tutorial. arXiv preprint arXiv:1907.11908 (2019)
  4. 4.
    Blum A, Ligett K, Roth A (2013) A learning theory approach to noninteractive database privacy. Journal of the ACM (JACM) 60(2):12MathSciNetCrossRefGoogle Scholar
  5. 5.
    Chen, R., Li, H., Qin, A.K., Kasiviswanathan, S.P., Jin, H.: Private spatial data aggregation in the local setting. In: 2016 IEEE 32nd International Conference on Data Engineering (ICDE), pp. 289–300. IEEE (2016)Google Scholar
  6. 6.
    Colesky, M., Hoepman, J.H.: Privacy patterns (2017). https://privacypatterns.org
  7. 7.
    Dwork, C., Feldman, V.: Privacy-preserving prediction. arXiv preprint arXiv:1803.10266 (2018)
  8. 8.
    Dwork, C., Roth, A., et al.: The algorithmic foundations of differential privacy. Found. Trends® Theor. Comput. Sci. 9(3–4), 211–407 (2014)Google Scholar
  9. 9.
    Fawaz, K., Feng, H., Shin, K.G.: Anatomization and protection of mobile apps’ location privacy threats. In: 24th \(\{\)USENIX\(\}\) Security Symposium (\(\{\)USENIX\(\}\) Security 15), pp. 753–768 (2015)Google Scholar
  10. 10.
    He, X., Machanavajjhala, A., Ding, B.: Blowfish privacy: tuning privacy-utility trade-offs using policies. In: Proceedings of the 2014 ACM SIGMOD international Conference on Management of Data, pp. 1447–1458. ACM (2014)Google Scholar
  11. 11.
    Hoepman, J.H.: Privacy design strategies (the little blue book) (2018)Google Scholar
  12. 12.
    Huang, C., Kairouz, P., Chen, X., Sankar, L., Rajagopal, R.: Generative adversarial privacy. arXiv preprint arXiv:1807.05306 (2018)
  13. 13.
    Kifer D, Machanavajjhala A (2014) Pufferfish: a framework for mathematical privacy definitions. ACM Trans. Database Syst. (TODS) 39(1):3MathSciNetCrossRefGoogle Scholar
  14. 14.
    Lin, B.R., Kifer, D.: Towards a systematic analysis of privacy definitions. J. Privacy Confidenti. 5(2) (2014)Google Scholar
  15. 15.
    Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: l-diversity: Privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data (TKDD) 1(1), 3–es (2007)Google Scholar
  16. 16.
    Padakandla, A., Kumar, P., Szpankowski, W.: The trade-off between privacy and fidelity via ehrhart theory. IEEE Trans. Inform. Theory (2019)Google Scholar
  17. 17.
    Rauf, A., Shaikh, R.A., Shah, A.: Security and privacy for iot and fog computing paradigm. In: 2018 15th Learning and Technology Conference (L&T), pp. 96–101. IEEE (2018)Google Scholar
  18. 18.
    Regulation GDP (2016) Regulation (eu) 2016/679 of the european parliament and of the council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46. Off. J. Eur. Union (OJ) 59(1–88):294Google Scholar
  19. 19.
    Samarati P (2001) Protecting respondents identities in microdata release. IEEE Tran. Knowl. Data Eng. 13(6):1010–1027CrossRefGoogle Scholar
  20. 20.
    Shalev-Shwartz, S., Shamir, O., Srebro, N., Sridharan, K.: Learnability, stability and uniform convergence. J. Mach. Learn. Res. 11, 2635–2670 (2010)Google Scholar
  21. 21.
    Ullah I, Shah MA, Wahid A, Mehmood A, Song H (2018) Esot: a new privacy model for preserving location privacy in internet of things. Telecommun. Syst. 67(4):553–575CrossRefGoogle Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.School of Information Technology and Engineering, VITVelloreIndia
  2. 2.Department of Software System and EngineeringSchool of Information Technology and Engineering, VITVelloreIndia

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