Abstract
Data privacy or safeguarding data from potential threats has become a critical issue in our data-centric world. Among the developed mechanisms catering to the objective of privacy preservation, differential privacy has emerged as a popular and effective technique which provides the required level of user privacy. In our work, we have information theoretically analyzed differential privacy in a multiple query-response based environment. We have evaluated our model on a real-world database and subsequently evaluated the effects of externally added noise on the resulting privacy. The simulated results confirm the notion that the privacy risk is inversely proportional to the amount of noise added in the system (defined by \( \varepsilon \)).
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References
U.E.I. Authority: EIA data set. http://www.eia.doe.gov/cneaf/electricity/page/eia826.html
Brand, R., Domingo-Ferrer, J., Mateo-Sanz, J.M.: Reference data sets to test and compare SDC methods for protection of numerical micro-data. Technical report, April 2002
Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006). https://doi.org/10.1007/11787006_1
Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006). https://doi.org/10.1007/11681878_14
Hundepool, A.: The CASC project. In: Domingo-Ferrer, J. (ed.) Inference Control in Statistical Databases. LNCS, vol. 2316, pp. 172–180. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47804-3_14
Li, N., Li, T., Venkatasubramanian, S.: t-Closeness: privacy beyond k-anonymity and l-diversity. In: 2007 IEEE 23rd International Conference on Data Engineering, pp. 106–115, April 2007
McSherry, F.D.: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, SIGMOD 2009, pp. 19–30. ACM, New York (2009)
Rebollo-Monedero, D., Forne, J., Domingo-Ferrer, J.: From t-Closeness-Like privacy to postrandomization via information theory. IEEE Trans. Knowl. Data Eng. 22(11), 1623–1636 (2010)
Sarathy, R., Muralidhar, K.: Evaluating laplace noise addition to satisfy differential privacy for numeric data. Trans. Data Privacy 4(1), 1–17 (2011)
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Chakraborty, B., Sadhya, D., Verma, S., Singh, K.P. (2019). Information Theoretic Analysis of Privacy in a Multiple Query-Response Based Differentially Private Framework. In: Verma, S., Tomar, R., Chaurasia, B., Singh, V., Abawajy, J. (eds) Communication, Networks and Computing. CNC 2018. Communications in Computer and Information Science, vol 839. Springer, Singapore. https://doi.org/10.1007/978-981-13-2372-0_23
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DOI: https://doi.org/10.1007/978-981-13-2372-0_23
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