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Privately Evaluating Decision Trees and Random Forests

. The decision Diffie-Hellman problem. In ANTS, pages 48-63, 1998. [14] J. Bos, C. Costello, P. Longa, and M. Naehrig. Specification of curve selection and supported curve parameters in MSR ECCLib. Technical Report MSR-TR-2014-92, Microsoft Research, June 2014. [15] J. W. Bos, C. Costello, P. Longa, and M. Naehrig. Selecting elliptic curves for cryptography: An efficiency and security analysis. IACR Cryptology ePrint Archive, 2014:130, 2014. [16] J. W. Bos, K. E. Lauter, and M. Naehrig. Private predictive analysis on

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Efficient Server-Aided 2PC for Mobile Phones

two-party computation via cut-and-choose oblivious transfer. In Yuval Ishai, editor, TCC, volume 6597 of Lecture Notes in Computer Science, pages 329-346. Springer, 2011. [LR14] Yehuda Lindell and Ben Riva. Cut-and-choose yaobased secure computation in the online/offline and batch settings. In Advances in Cryptology-CRYPTO 2014, pages 476-494. Springer, 2014. [MF06] Payman Mohassel and Matthew K. Franklin. Efficiency tradeoffs for malicious two-party computation. In Moti Yung, Yevgeniy Dodis, Aggelos Kiayias, and Tal Malkin, editors

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Differentially Private Oblivious RAM

Hubert Chan, Kai-Min Chung, Bruce Maggs, and Elaine Shi. Foundations of differentially oblivious algorithms. https://eprint.iacr.org/2017/1033.pdf , 2018. [14] Binyi Chen, Huijia Lin, and Stefano Tessaro. Oblivious parallel ram: improved efficiency and generic constructions. In Theory of Cryptography Conference (TCC) , 2016. [15] Benny Chor, Eyal Kushilevitz, Oded Goldreich, and Madhu Sudan. Private information retrieval. J. ACM , 45(6), 1998. [16] Paul Cuff and Lanqing Yu. Differential privacy as a mutual information constraint. In ACM Conference

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Parallel Oblivious Array Access for Secure Multiparty Computation and Privacy-Preserving Minimum Spanning Trees

. Bioinformatics , 18(4):536–545, 2002. [57] A. C.-C. Yao. Protocols for secure computations (extended abstract). In FOCS , pages 160–164. IEEE, 1982. [58] S. Zahur and D. Evans. Circuit structures for improving efficiency of security and privacy tools. In 2013 IEEE Symposium on Security and Privacy, SP 2013, Berkeley, CA, USA, May 19-22, 2013 , pages 493–507. IEEE Computer Society, 2013.

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Privacy-Preserving Distributed Linear Regression on High-Dimensional Data

. Huang, and E. Shi. ObliVM: A programming framework for secure computation. In IEEE Symposium on Security and Privacy , pages 359–376. IEEE Computer Society, 2015. [52] G. Meurant. The Lanczos and conjugate gradient algorithms: from theory to finite precision computations , volume 19. SIAM, 2006. [53] P. Mohassel and M. K. Franklin. Efficiency tradeoffs for malicious two-party computation. In Public Key Cryptography , volume 3958 of Lecture Notes in Computer Science , pages 458–473. Springer, 2006. [54] K. P. Murphy. Machine learning: a

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Mitigating Location Privacy Attacks on Mobile Devices using Dynamic App Sandboxing

mobility model for human walks. In IEEE INFOCOM 2009 , April 2009. [54] Baochun Li. On increasing service accessibility and efficiency in wireless ad-hoc networks with group mobility. Wirel. Pers. Commun. , April 2002. [55] Bin Liu, Mads Schaarup Andersen, Florian Schaub, Hazim Almuhimedi, Shikun (Aerin) Zhang, Norman Sadeh, Yuvraj Agarwal, and Alessandro Acquisti. Follow my recommendations: A personalized privacy assistant for mobile app permissions. In Twelfth Symposium on Usable Privacy and Security (SOUPS 2016) , Denver, CO, 2016. USENIX Association

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