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Randomized response, as a basic building-block for differentially private mechanism, has given rise to great interest and found various potential applications in science communities.
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J. J. A. Moors. “Optimization of the unrelated question randomized response model”. Journal of the American Statistical Association. vol. 66, no. 335, pp. 627-629 (1971)
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N. S. Mangat. “An improved randomized response strategy”. Journal of the Royal Statistical Society. Series B, Methodological. vol. 56, no. 1, pp. 93-95 (1994)
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W. L. Du, Z. J. Zhan. “Using randomized response techniques for privacy-preserving data mining”. In the ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). pp. 505-510 (2003)
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J. J. Donovan, S. A. Dwight, and G. M. Hurtz. “An assessment of the prevalence, severity, and verifiability of entry-level applicant faking using the randomized response technique”. Human Performance. vol. 16, no. 1, pp. 81-106 (2003)
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C. Dwork. “Differential privacy”. In Automata, Languages and Programming (ICALP). pp. 1-12 (2006)
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C. Dwork, F. McSherry, K. Nissim, and A. Smith. “Calibrating noise to sensitivity in private data analysis”. In Theory of Cryptography (TCC). pp. 265-284 (2006)
2006
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Z. L. Huang, W. L. Du. “OptRR: Optimizing randomized response schemes for privacy-preserving data mining”. In International Conference on Data Engineering (ICDE). pp. 705-714 (2008)
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J. A. Bondy, U. S. R. Murty. “Graph Theory”. Springer. (2008)
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D. W. Gingerich. “Understanding off-the-books politics: Conducting inference on the determinants of sensitive behavior with randomized response surveys”. Political Analysis. vol. 18, no. 3, pp. 349-380 (2010)
2010
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J. C. Duchi, M. I. Jordan, and M. J. Wainwright. “Local privacy and statistical minimax rates”. In Annual IEEE Symposium on Foundations of Computer Science (FOCS). pp. 429-438 (2013)
2013
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U. Erlingsson, V. Pihur, and A. Korolova. “Rappor: Randomized aggregatable privacy-preserving ordinal response”. In ACM SIGSAC Conference on Computer and Communications Security (CCS). pp. 1054-1067 (2014)
2014
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J. C. Duchi, M. I. Jordan, and M. J. Wainwright. “Privacy aware learning”. Journal of the ACM. vol. 61, no. 6, pp. 1-57 (2014)
2014
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C. Dwork, A. Roth. “The algorithmic foundations of differential privacy”. Foundations and Trends in Theoretical Computer Science. vol. 9, no. 3-4, pp. 211-407 (2014)
2014
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P. Kairouz, S. Oh, and P. Viswanath. “Extremal mechanisms for local differential privacy”. In Advances in Neural Information Processing Systems (NIPS). pp. 2879-2887 (2014)
2014
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X. Chen, Q. Du, Z. Jin, T. Xu, J. Shi, and G. Gao. “The randomized response technique application in the survey of homosexual commercial sex among men in Beijing”. Iranian Journal of Public Health. vol. 43, no. 4, pp. 416-422 (2014)
2014
Cited alongside, same era.
R. Bassily, A. Smith. “Local, private, efficient protocols for succinct histograms”. In Annual ACM Symposium on Theory of Computing (STOC). pp. 127-135 (2015)
2015
Cited alongside, same era.
H. Ogasawara. “Bias Adjustment Minimizing the Asymptotic Mean Square Error”. Communications in Statistics-Theory and Methods. vol. 44, no. 16, pp. 3503-3522 (2015)
2015
Cited alongside, same era.
Apple’s ‘differential privacy’ is about collecting your data-but not your data. Wired, Jun 13, (2016)
2016
Cited alongside, same era.
Y. Wang, X. Wu, and D. Hu. “Using randomized response for differential privacy preserving data collection”. CEUR Workshop Proceedings. (2016)
2017
Later among the works it cites.
A. Cuzzocrea, E. Damiani. “Pedigree-ing Your Big Data: Data-Driven Big Data Privacy in Distributed Environments”. In 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pp. 675-681 (2018)
2018
Later among the works it cites.
T. Wang, N. Li, and S. Jha. “Locally differentially private frequent itemset mining”. In 2018 IEEE Symposium on Security and Privacy (SP). pp. 127-143, (2018)
2018
Later among the works it cites.
M. E. J. Newman. “Networks: An Introduction”. Oxford University Press. (2018)
2018
Later among the works it cites.
S. Rubinstein-Salzedo. “Cryptography”. Springer. (2018)
2018
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2016
Cited alongside, same era.
P. Kairouz, K. Bonawitz, and D. Ramage. “Discrete distribution estimation under local privacy”. In 33rd International Conference on Machine Learning (ICML). pp. 2436-2444 (2016)
2016
Cited alongside, same era.
2016
Cited alongside, same era.
A.-L. Barabási. “Network Science”. Cambridge University Press. (2016)
2016
Cited alongside, same era.
S. Raskhodnikova, A. Smith. “Lipschitz Extensions for Node-Private Graph Statistics and the Generalized Exponential Mechanism”. In 57th Annual Symposium on Foundations of Computer Science (FOCS). pp. 495-504 (2016)
2016
Cited alongside, same era.
W-Y. Day, N. H. Li, and M. Lyu. “Publishing Graph Degree Distribution with Node Differential Privacy”. In ACM SIGMOD International Conference on Management of Data. pp. 123-138 (2016)
2016
Cited alongside, same era.
B. Ding, J. Kulkarni, and S. Yekhanin. “Collecting telemetry data privately”. In Advances in Neural Information Processing Systems (NIPS). pp. 3574-3583 (2017)
2017
Cited alongside, same era.
H. Corrigan-Gibbs, D. Boneh. “Prio: Private, robust, and scalable computation of aggregate statistics”. In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI). pp. 259-282 (2017)
2017
Cited alongside, same era.
Later among the works it cites.
S. Salloum, J. Z. Huang, and Y. L. He. “Random Sample Partition: A Distributed Data Model for Big Data Analysis”. IEEE Transactions on Industrial Informatics. vol. 15, no. 11, pp. 5846-5854 (2019)
2019
Later among the works it cites.
Q. Q. Ye, H. B. Hu, X. F. Meng, and H. D. Zheng. “PrivKV: Key-Value Data Collection with Local Differential Privacy”. In 2019 IEEE Symposium on Security and Privacy (SP). pp. 317-331 (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
H. P. Sun, X. K. Xiao, I. Khalil, Y. Yang, Z. Qin, H. Wang, and T. Yu. “Analyzing Subgraph Statistics from Extended Local Views with Decentralized Differential Privacy”. In ACM SIGSAC Conference on Computer and Communications Security (CCS). pp. 703-717 (2019)
2019
Later among the works it cites.
C. G. Xu, J. Ren, D. Y. Zhang, Y. X. Zhang, Z. Qin, and K. Ren. “GANobfuscator: Mitigating Information Leakage Under GAN via Differential Privacy”. IEEE Transactions on Information Forensics and Security. vol. 14, no. 9, pp. 2358-2371 (2019)
2019
Later among the works it cites.
X. L. Gu, M. Li, Y. Q. Cheng, L. Xiong, and Y. Cao. “PCKV: Locally Differentially Private Correlated Key-Value Data Collection with Optimized Utility. In 29th USENIX Security Symposium. pp. 967-984 (2020)
2020
Later among the works it cites.
H. N. Song, T. Luo, X. Wang, and J. F. Li. “Multiple Sensitive Values-Oriented Personalized Privacy Preservation Based on Randomized Response”. IEEE Transactions on Information Forensics and Security. vol. 15, pp. 2209-2224 (2020)
2020
Later among the works it cites.
C. K. Wei, S. L. Ji, C. C. Liu, W. Z. Chen, and T. Wang. “AsgLDP: Collecting and Generating Decentralized Attributed Graphs With Local Differential Privacy”. IEEE Transactions on Information Forensics and Security. vol. 15, pp. 3239-3254 (2020)
2020
Later among the works it cites.
T. Guo, R. D. Zhou, and C. Tian. “On the Information Leakage in Private Information Retrieval Systems”. IEEE Transactions on Information Forensics and Security. vol. 15, pp. 2999-3012 (2020)
2020
Later among the works it cites.
R. D. Zhang, N. Zhang, A. Moini, W. J. Lou, and Y. T. Hou. “PrivacyScope: Automatic Analysis of Private Data Leakage in TEE-Protected Applications”. In 40th International Conference on Distributed Computing Systems (ICDCS). pp. 34-44 (2020)
2020
Later among the works it cites.
E. Yoshikawa, N. Takizawa, H. Kikuchi, T. Mega, and T. Ushio. “An Estimator for Weather Radar Doppler Power Spectrum via Minimum Mean Square Error”. IEEE Transactions on Geoscience and Remote Sensing. (Early Access Article) DOI:10.1109/TGRS.2020.3044111 (2021)
2021
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