The Role of Information Theory in the Field of Big Data Privacy

Authors

  • Mariam E. Haroutunian Institute for Informatics and Automation Problems of NAS RA
  • Karen A. Mastoyan Gavar State University

DOI:

https://doi.org/10.51408/1963-0071

Keywords:

Big data, Anonymization, Differential privacy, Entropy, Mutual information, Distortion

Abstract

Protecting privacy in Big Data is a rapidly growing research area. The first approach towards privacy assurance was the anonymity method. However, recent research indicated that simply anonymized data sets can be easily attacked. Later, differential privacy was proposed, which proved to be the most promising approach. The trade-off between privacy and the usefulness of published data, as well as other problems, such as the availability of metrics to compare different ways of achieving anonymity, are in the realm of Information Theory. Although a number of review articles are available in literature, the information - theoretic methods capacities haven’t been paid due attention. In the current article an overview of state-of-the-art methods from Information Theory to ensure privacy are provided.

References

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.

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 1

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.

C. Dwork and A. Roth, “The Algorithmic Foundations of Differential Privacy”, Foundations and Trends in Theoretical Computer Science: vol. 9, no. 3-4, pp 211–407. 2014. http://dx.doi.org/10.1561/0400000042

N. Li, M. Lyu, D. Su and W. Yang, Differential Privacy: From Theory to Practice, Morgan & Claypool, 2016. doi: 10.2200/S00735ED1V01Y201609SPT018.

X. Yao, X. Zhou and J. Ma, “Differential Privacy of Big Data: An Overview,” IEEE 2nd Intern. Conf. on Big Data Security on Cloud (BigDataSecurity), IEEE Intern. Conf. on High Performance and Smart Computing (HPSC), and IEEE Intern. Conf. on Intelligent Data and Security (IDS), New York, NY, USA, pp. 7–12, 2016. doi: 10.1109/BigDataSecurity-HPSC-IDS.2016.9.

A. Serjantov and G. Danezis, “Towards an Information Theoretic Metric for Anonymity”. In: Dingledine R., Syverson P. (eds) Privacy Enhancing Technologies, Lecture Notes in Computer Science, vol 2482. Springer, Berlin, Heidelberg, 2003. https://doi.org/10.1007/3-540-36467-6-4

P. Venkitasubramaniam, T. He and L. Tong, “Anonymous networking amidst eavesdroppers,” IEEE Trans. on Information Theory, vol. 54, no. 6, pp. 2770–2784, June 2008. doi: 10.1109/TIT.2008.921660.

S. Zhou, J. Lafferty and L. Wasserman, “Compressed and privacy-sensitive sparse regression,” IEEE Trans. on Information Theory, vol. 55, no. 2, pp. 846–866, Feb. 2009. doi: 10.1109/TIT.2008.2009605.

D. Rebollo-Monedero, J. Forne, and J. Domingo-Ferrer, “From t-closeness-like privacy to postrandomization via Information Theory”, IEEE Trans. on Knowl. and Data Eng., vol. 22, no. 11, pp. 1623-1636, 2010. DOI:https://doi.org/10.1109/TKDE.2009.190

D. Bernhard, V. Cortier, O. Pereira, and B. Warinschi, “Measuring vote privacy, revisited”, Proc. of ACM conf. on Computer and Communications Security, Association for Computing Machinery, New York, NY, USA, pp. 941952, 2012. DOI:https://doi.org/10.1145/2382196.2382295

A. Sarwate and L. Sankar, "A rate-distortion perspective on local differential privacy", 52 annual Allerton conf., UIUC, Illinois, USA, pp. 903-908, 2014.

W. Wang, L. Ying and J. Zhang, "On the relation between identifiability, differential privacy, and mutual-information privacy, "IEEE Trans. on Information Theory, vol. 62, no. 9, pp. 5018-5029, Sept. 2016. doi: 10.1109/TIT.2016.2584610.

K. Kalantari, L. Sankar and A. D. Sarwate, "Optimal differential privacy mechanisms under Hamming distortion for structured source classes,"IEEE Intern. Symp. on Information Theory, Barcelona, Spain, pp. 2069-2073, 2016. doi: 10.1109/ISIT.2016.7541663.

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Published

2021-12-16

How to Cite

Haroutunian, M. E., & Mastoyan, K. A. (2021). The Role of Information Theory in the Field of Big Data Privacy. Mathematical Problems of Computer Science, 55, 35–43. https://doi.org/10.51408/1963-0071