Optimized Differential Private Online Transaction Scheme for Online Shopping

[1] J. Claessens, V. Dem, D. De Cock, B. Preneel, and J. Vandewalle, “On the security of today’s online electronic banking systems,”
Comput.Secur., vol. 21, no. 3, pp. 253–265, 2002.
[2] S. Kiljan, H. P. E. Vranken, and M. C. J. D. van Eekelen, “Evaluation of transaction authentication methods for online banking,”
Future Gener.Comput.Syst., vol. 80, pp. 430–447, 2018.
[3] R. Ganesan et al., “A secured hybrid architecture model for internet banking (e-banking),” The J. Internet Banking Commerce, vol. 14,
no. 1, pp. 1–17, 1970.
[4] M. Tebaa, K. Zkik, and S. El Hajji, “Hybrid homomorphic encryption method for protecting the privacy of banking data in the cloud,”
Int. J. Secur.Appl., vol. 9, no. 6, pp. 61–70, 2015.
[5] Z. Zhang, Z. Qin, L. Zhu, J. Weng, and K. Ren, “Cost-friendly differential privacy for smart meters: Exploiting the dual roles of the
noise,” IEEE Trans. Smart Grid, vol. 8, no. 2, pp. 619–626,Mar. 2017.
[6] M. Hardt and K. Talwar, “On the geometry of differential privacy,” in Proc. 42ndACMSymp. Theory Comput., 2010, pp. 705–714.
[7] S. Meiser and E. Mohammadi, “Tight on budget?Tight bounds for r-fold approximate differential privacy,” in Proc. ACM SIGSAC
Conf. Comput.Commun.Secur., 2018, pp. 247–264.
[8] F. Farokhi and H. Sandberg, “Ensuring privacy with constrained additive noise by minimizing fisher information,” Automatica, vol.
99, pp. 275–288, 2019.
[9] J. Soria-Comas, J. Domingo-Ferrer, D. S_anchez, and D. Meg_ıas, “Individual differential privacy: A utility-preserving formulation of
differential privacy guarantees,” IEEE Trans. Inf. Forensics Security, vol. 12, no. 6, pp. 1418–1429, Jun. 2017.
[10] T. Zhu, P. Xiong, G. Li, and W. Zhou, “Correlated differential privacy: Hiding information in non-IID data set,” IEEE Trans. Inf.
Forensics Security, vol. 10, no. 2, pp. 229–242, Feb. 2015.
[11] M. Fanaeepour and B. I. P. Rubinstein, “Histogramming privately ever after: Differentially-private data-dependent error bound
optimisation,” in Proc. IEEE 34th Int. Conf.Data Eng., 2018, pp. 1204–1207.
[12] K. Chaudhuri, J. Imola, and A. Machanavajjhala, “Capacity bounded differential privacy,” in Proc. Int. Conf. Neural Inf. Process.
Syst., 2019, pp. 3469–3478.
[13] Z. Zhang, W. Cao, Z. Qin, L. Zhu, Z. Yu, and K. Ren, “When privacy meets economics: Enabling differentially-private
batterysupported meter reporting in smart grid,” in Proc. IEEE/ACM 25th Int. Symp. Quality Service, 2017, pp. 1–9.
[14] D. Cynthia and L. Jing, “Differential privacy and robust statistics,” in Proc. 41st ACM Symp. Theory Comput., 2009, pp. 371–380.
[15] D. Cynthia, “Differential privacy,” in Proc. Automata Lang. Program., 2006, pp. 1–10.
[16] M. Chiang and T. Zhang, “Fog and IoT: An overview of research opportunities,” IEEE Internet Things J., vol. 3, no. 6, pp. 854–864,
Dec. 2016.
[17] C. Dwork, “Differential privacy: A survey of results,” in Proc. Int. Conf. Theory Appl. Models Comput., 2008, pp. 1–19.
[18] T. Zhu, G. Li, W. Zhou, and P. S. Yu, “Differentially private data publishing and analysis: A survey,” IEEE Trans. Knowl. Data
Eng.,vol. 29, no. 8, pp. 1619–1638, Aug. 2017.