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Manipulation attacks in local differential privacy. ArXiv, abs\/1909.09630, 2019."},{"key":"e_1_3_2_1_23_1","volume-title":"Paper 2018\/570","author":"Chida K.","year":"2018","unstructured":"K. Chida , D. Genkin , K. Hamada , D. Ikarashi , R. Kikuchi , Y. Lindell , and A. Nof . Fast large-scale honest-majority MPC for malicious adversaries. Cryptology ePrint Archive , Paper 2018\/570 , 2018 . https:\/\/eprint.iacr.org\/2018\/570. K. Chida, D. Genkin, K. Hamada, D. Ikarashi, R. Kikuchi, Y. Lindell, and A. Nof. Fast large-scale honest-majority MPC for malicious adversaries. Cryptology ePrint Archive, Paper 2018\/570, 2018. https:\/\/eprint.iacr.org\/2018\/570."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00145-019-09319-x"},{"key":"e_1_3_2_1_25_1","volume-title":"Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI)","author":"Corrigan-Gibbs H.","year":"2017","unstructured":"H. Corrigan-Gibbs and D. Boneh . 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In Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), 2017."},{"key":"e_1_3_2_1_28_1","article-title":"Free gap estimates from the exponential mechanism, sparse vector, noisy max and related algorithms","author":"Ding Z.","year":"2022","unstructured":"Z. Ding , Y. Wang , Y. Xiao , G. Wang , D. Zhang , and D. Kifer . Free gap estimates from the exponential mechanism, sparse vector, noisy max and related algorithms . VLDB Journal , Feb. 2022 . Z. Ding, Y. Wang, Y. Xiao, G. Wang, D. Zhang, and D. Kifer. Free gap estimates from the exponential mechanism, sparse vector, noisy max and related algorithms. VLDB Journal, Feb. 2022.","journal-title":"VLDB Journal"},{"key":"e_1_3_2_1_29_1","volume-title":"Proceedings of the Conference on Neural Information Processing Systems (NeurIPS)","author":"Durfee D.","year":"2019","unstructured":"D. Durfee and R. Rogers . Practical differentially private top-k selection with pay-what-you-get composition . 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First International Conference on Availability, Reliability and Security","author":"Gopinath K.","year":"2006","unstructured":"K. Gopinath and V. H. Gupta . An extended verifiable secret redistribution protocol for archival systems . In Proc. First International Conference on Availability, Reliability and Security , 2006 . K. Gopinath and V. H. Gupta. An extended verifiable secret redistribution protocol for archival systems. In Proc. First International Conference on Availability, Reliability and Security, 2006."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-49896-5_11"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-012722442-8\/50036-7"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372297.3417269"},{"key":"e_1_3_2_1_39_1","volume-title":"Proceedings of the ACM Conference on Computer and Communications Security (CCS)","author":"Keller M.","year":"2020","unstructured":"M. Keller . MP-SPDZ : A versatile framework for multi-party computation . In Proceedings of the ACM Conference on Computer and Communications Security (CCS) , 2020 . M. Keller. MP-SPDZ: A versatile framework for multi-party computation. In Proceedings of the ACM Conference on Computer and Communications Security (CCS), 2020."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/1132516.1132532"},{"key":"e_1_3_2_1_41_1","volume-title":"The Byzantine generals problem. ACM Transactions on Programming Languages and Systems (TOPLAS), 4(3):382--401","author":"Lamport L.","year":"1982","unstructured":"L. Lamport , R. Shostak , and M. Pease . The Byzantine generals problem. ACM Transactions on Programming Languages and Systems (TOPLAS), 4(3):382--401 , 1982 . L. Lamport, R. Shostak, and M. Pease. The Byzantine generals problem. 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Google AI Blog, https:\/\/ai.googleblog.com\/2020\/05\/federated-analytics-collaborative-data.html."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/1807167.1807247"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-78086-9_27"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/1831407.1831427"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477132.3483585"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341301.3359660"},{"key":"e_1_3_2_1_54_1","volume-title":"Proceedings of the USENIX Symposium on Operating Systems Design and Implementation (OSDI)","author":"Roth E.","year":"2020","unstructured":"E. Roth , H. Zhang , A. Haeberlen , and B. C. Pierce . Orchard: Differentially private analytics at scale . In Proceedings of the USENIX Symposium on Operating Systems Design and Implementation (OSDI) , 2020 . E. Roth, H. Zhang, A. Haeberlen, and B. C. Pierce. Orchard: Differentially private analytics at scale. In Proceedings of the USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2020."},{"key":"e_1_3_2_1_55_1","volume-title":"Proceedings of the Network and Distributed System Security Symposium (NDSS)","author":"Shi E.","year":"2011","unstructured":"E. Shi , T.-H. H. Chan , E. G. Rieffel , R. Chow , and D. X. Song . Privacy-preserving aggregation of time-series data . In Proceedings of the Network and Distributed System Security Symposium (NDSS) , 2011 . E. Shi, T.-H. H. Chan, E. G. Rieffel, R. Chow, and D. X. Song. Privacy-preserving aggregation of time-series data. In Proceedings of the Network and Distributed System Security Symposium (NDSS), 2011."},{"key":"e_1_3_2_1_56_1","unstructured":"A. Smith. Differential privacy and the secrecy of the sample Sept. 2009. https:\/\/adamdsmith.wordpress.com\/2009\/09\/02\/sample-secrecy\/.  A. Smith. Differential privacy and the secrecy of the sample Sept. 2009. https:\/\/adamdsmith.wordpress.com\/2009\/09\/02\/sample-secrecy\/."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3302424.3303982"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/SFCS.1982.38"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341697"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/2504730.2504752"},{"key":"e_1_3_2_1_61_1","volume-title":"Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI)","author":"Zheng W.","year":"2017","unstructured":"W. Zheng , A. Dave , J. G. Beekman , R. A. Popa , J. E. Gonzalez , and I. Stoica . Opaque: An oblivious and encrypted distributed analytics platform . In Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI) , 2017 . W. Zheng, A. Dave, J. G. Beekman, R. A. Popa, J. E. Gonzalez, and I. Stoica. 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