Differential Privacy in Practice: Use Cases


  • Karen A. Mastoyan Gavar State University




Big data, Differential privacy, R environment


The problem of ensuring privacy is relevant in connection with the development of big data technologies. One of the modern and most promising methods of privacy protection is the differential privacy. In this paper the differential privacy applications developed by big companies are investigated. The libraries’ capabilities and tools of Google, IBM, as well as packages in R are analyzed. The differential privacy process for data collected from users implemented by Apple is studied.


A. Mehmood, I. Natgunanathan, Y. Xiang, G. Hua and S. Guo, “Protection of Big Data privacy”, IEEE Access, vol. 4, pp. 1821–1834, 2016, doi: 10.1109/ACCESS. 2016.2558446.

L. Xu, C. Jiang, J. Wang, J. Yuan and Y. Ren, “Information security in Big Data: Privacy and data mining” IEEE Access, vol. 2, pp. 1149–1176, 2014, doi: 10.1109/ACCESS. 2014.2362522.

S. Yu, “Big Privacy: Challenges and opportunities of privacy study in the age of Big Data”, IEEE Acces, vol. 4, pp. 2751–2763, 2016, doi: 10.1109/ACCESS.2016.2577036.

M. Haroutunian and K. Mastoyan, “The role of information theory in the field of Big Data privacy, Mathematical Problems of Computer Science, vol. 55, pp. 45 - 53, 2021.

C. Dwork, M. Bugliesi, B. Preneel, V. Sassone, I.Wegener (eds) Automata, ”Differential Privacy”, Languages and Programming. ICALP, Lecture Notes in Computer Science, vol 4052. Springer, Berlin, Heidelberg, 2006. https://doi.org/10.1007/11787006

C. Dwork and A. Roth, “The algorithmic foundations of differential privacy”, Foundations and Trends in Theoretical Computer Science, vol. 9, no.3-4, pp. 211407, 2014.

K. M. P. Shrivastva, M. A. Rizvi and S. Singh, “Big Data privacy based on differential privacy a hope for Big Data,” Proc. Intern. Conf. on Computational Intelligence and Communication Networks, Bhopal, India, pp. 776–781, 2014. doi:10.1109/CICN.2014.167.

Differential Privacy Team, Apple, Learning with Privacy at Scale, [Online]. Available: https://docs-assets.developer.apple.com/ml-research/papers/learning-withprivacy-at-scale.pdf

End-to-end differential privacy solution, [Online]. Available: https://github.com/google/differential-privacy/tree/main/privacy-on-beam

Google Developers, Google, Enabling developers and organizations to use differential privacy, [Online]. Available: https://developers.googleblog.com/2019/09/enablingdevelopers-and-organizations.html

Naoise Holohan, Stefano Braghin, Pol Mac Aonghusa and Killian Levacher. Diffprivlib: The IBM Dierential Privacy Library, 2019 https://arxiv.org/pdf/1907.02444.pdf

Differential privacy package using R, [Online]. Available: https://github.com/brubinstein/diffpriv

B. I. P. Rubinstein and A. Francesco, “diffpriv: An R package for easy differential privacy”, Journal of Machine Learning Research, vol. 18, pp. 1-5, 2017.




How to Cite

Mastoyan, K. A. (2021). Differential Privacy in Practice: Use Cases. Mathematical Problems of Computer Science, 56, 48–55. https://doi.org/10.51408/1963-0078