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Chapter 2

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Chapter 3

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Chapter 4

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[3] T. Wang, J. Blocki, and N. Li, “Locally Differentially Private Protocols for Frequency Estimation,” USENIX Secur., 2017.

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Chapter 5

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Chapter 6

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Chapter 7

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Chapter 8

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[8] P. Grubbs, T. Ristenpart, and V. Shmatikov, “Why Your Encrypted Database Is Not Secure,” Proc. 16th Work. Hot Top. Oper. Syst. - HotOS ’17, pp. 162-168, 2017, doi: 10.1145/3102980.3103007.

[9] M.S. Lacharite, B. Minaud, and K.G. Paterson, “Improved Reconstruction Attacks on Encrypted Data Using Range Query Leakage,” Proc. - IEEE Symp. Secur. Priv., pp. 297-314, 2018, doi: 10.1109/SP.2018.00002.

[10] M. Hosenball, “Swiss spy agency warns U.S., Britain about huge data leak,” Reuters, December 4, 2012. https://www.reuters.com/article/us-usa-switzerland-datatheft/swiss-spy-agency-warns-u-s-britain-about-huge-data-leak-idUSBRE8B30ID20121204 (accessed January 15, 2019).

[11] C. Terhune, “Nearly 5,000 patients affected by UC Irvine medical data breach,” Los Angeles Times, June 18, 2015. https://www.latimes.com/business/la-fi-uc-irvine-data-breach-20150618-story.html (accessed Jan. 15, 2019).

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Chapter 9

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[4] K. Diamantaras and S.Y. Kung, “Data Privacy Protection by Kernel Subspace Projection and Generalized Eigenvalue Decomposition,” IEEE Int. Work. Mach. Learn. Signal Process. MLSP, 2016, doi: 10.1109/MLSP.2016.7738831.

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Chapter 10

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[5] A. Narayanan and V. Shmatikov, “Robust De-anonymization of Large Sparse Datasets,” Proc. - IEEE Symp. Secur. Priv., pp. 111-125, 2008, doi: 10.1109/SP.2008.33.

[6] S. Goryczka, L. Xiong, and V. Sunderam, “Secure multiparty aggregation with differential privacy: A comparative study,” ACM Int. Conf. Proceeding Ser., pp. 155-163, 2013, doi: 10.1145/2457317.2457343.

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Appendix

[1] 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, doi: 10.1561/0400000042.

[2] A. Ghosh, T. Roughgarden, and M. Sundararajan, “Universally Utility-Maximizing Privacy Mechanisms,” SIAM Journal on Computing, vol. 41, no. 6, pp. 1673-1693, 2012.

[3] Q. Geng and P. Viswanath, “The Optimal Mechanism in Differential Privacy,” 2014 IEEE International Symposium on Information Theory, 2014, pp. 2371-2375, doi: 10.1109/ISIT.2014.6875258.

[4] H. Imtiaz and A.D. Sarwate, “Symmetric Matrix Perturbation for Differentially-Private Principal Component Analysis,” 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, 2016, pp. 2339-2343, doi: 10.1109/ICASSP.2016.7472095.