Fetching the paper…
Reading the bibliography…
Local differential privacy (LDP) can provide each user with strong privacy guarantees under untrusted data curators while ensuring accurate statistics derived from privatized data.
Warner, S. L., 1965. Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association 60 (309), 63–69
1965
Earlier work this paper cites.
Marsaglia, G., Marsaglia, J. C., 1990. A new derivation of Stirling’s approximation to n! The American Mathematical Monthly 97 (9), 826–829
1990
Earlier work this paper cites.
Dwork, C., Lei, J., 2009. Differential privacy and robust statistics. In: Proc. ACM STOC. pp. 371–380
2009
Earlier work this paper cites.
McSherry, F. D., 2009. Privacy integrated queries: An extensible platform for privacy-preserving data analysis. In: Proc. ACM SIGMOD. pp. 19–30
2009
Earlier work this paper cites.
Chaudhuri, K., Monteleoni, C., Sarwate, A. D., 2011. Differentially private empirical risk minimization. Journal of Machine Learning Research 12 (Mar), 1069–1109
2011
Earlier work this paper cites.
Kasiviswanathan, S. P., Lee, H. K., Nissim, K., Raskhodnikova, S., Smith, A., 2011. What can we learn privately? SIAM Journal on Computing 40 (3), 793–826
2011
Earlier work this paper cites.
Duchi, J. C., Jordan, M. I., Wainwright, M. J., 2013. Local privacy and statistical minimax rates. In: Proc. FOCS. pp. 429–438
2013
Earlier work this paper cites.
Xu, J., Zhang, Z., Xiao, X., Yang, Y., Yu, G., Winslett, M., 2013. Differentially private histogram publication. VLDB 22 (6), 797–822
2013
Earlier work this paper cites.
Dwork, C., Roth, A., 2014. The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science 9 (3-4), 211–407
2014
Earlier work this paper cites.
Erlingsson, Ú., Pihur, V., Korolova, A., 2014. RAPPOR: Randomized aggregatable privacy-preserving ordinal response. In: Proc. ACM SIGSAC CCS. pp. 1054–1067
2014
Earlier work this paper cites.
Kairouz, P., Oh, S., Viswanath, P., 2014. Extremal mechanisms for local differential privacy. In: Advances in neural information processing systems. pp. 2879–2887
2014
Earlier work this paper cites.
Bassily, R., Smith, A., 2015. Local, private, efficient protocols for succinct histograms. In: Proc. ACM STOC. pp. 127–135
2015
Earlier work this paper cites.
Chen, R., Xiao, Q., Zhang, Y., Xu, J., 2015. Differentially private high-dimensional data publication via sampling-based inference. In: Proc. ACM SIGKDD. pp. 129–138
2015
Earlier work this paper cites.
Guo, B., Wang, Z., Yu, Z., Wang, Y., Yen, N. Y., Huang, R., Zhou, X., 2015. Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm. ACM Computing Surveys (CSUR) 48 (1), 7
2015
Earlier work this paper cites.
Han, Q., Liang, S., Zhang, H., 2015. Mobile cloud sensing, big data, and 5G networks make an intelligent and smart world. IEEE Network 29 (2), 40–45
2015
Earlier work this paper cites.
Hu, X., Yuan, M., Yao, J., Deng, Y., Chen, L., Yang, Q., Guan, H., Zeng, J., 2015. Differential privacy in telco big data platform. VLDB Endowment 8 (12), 1692–1703
2015
Earlier work this paper cites.
Yang, K., Zhang, K., Ren, J., Shen, X., 2015. Security and privacy in mobile crowdsourcing networks: Challenges and opportunities. IEEE communications magazine 53 (8), 75–81
2015
Cited alongside, same era.
Zhu, T., Xiong, P., Li, G., Zhou, W., 2015. Correlated differential privacy: Hiding information in non-IID data set. IEEE Trans. Inf. Forensics Security 10 (2), 229–242
2015
Cited alongside, same era.
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., Zhang, L., 2016. Deep learning with differential privacy. In: Proc. ACM SIGSAC CCS. pp. 308–318
2016
Cited alongside, same era.
Bun, M., Steinke, T., 2016. Concentrated differential privacy: Simplifications, extensions, and lower bounds. In: Theory of Cryptography Conference. pp. 635–658
2016
Cited alongside, same era.
Acs, G., Melis, L., Castelluccia, C., De Cristofaro, E., 2018. Differentially private mixture of generative neural networks. IEEE Trans. Knowl. Data Eng., 1–14
2018
Later among the works it cites.
Balle, B., Wang, Y.-X., 2018. Improving the Gaussian mechanism for differential privacy: Analytical calibration and optimal denoising. In: Proc. ICML. pp. 403–412
2018
Later among the works it cites.
2018
Later among the works it cites.
Cormode, G., Kulkarni, T., Srivastava, D., 2018. Marginal release under local differential privacy. In: Proc. ACM SIGMOD. pp. 131–146
2018
Later among the works it cites.
Duchi, J. C., Jordan, M. I., Wainwright, M. J., 2018. Minimax optimal procedures for locally private estimation. Journal of the American Statistical Association 113 (521), 182–201
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
Merlino, G., Arkoulis, S., Distefano, S., Papagianni, C., Puliafito, A., Papavassiliou, S., 2016. Mobile crowdsensing as a service: A platform for applications on top of sensing clouds. Future Generation Computer Systems 56, 623–639
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Phan, N., Wang, Y., Wu, X., Dou, D., 2016. Differential privacy preservation for deep auto-encoders: an application of human behavior prediction. In: Proc. AAAI. pp. 1309–1316
2016
Cited alongside, same era.
Qin, Z., Yang, Y., Yu, T., Khalil, I., Xiao, X., Ren, K., 2016. Heavy hitter estimation over set-valued data with local differential privacy. In: Proc. ACM SIGSAC CCS. pp. 192–203
2016
Cited alongside, same era.
Bittau, A., Erlingsson, Ú., Maniatis, P., Mironov, I., Raghunathan, A., Lie, D., Rudominer, M., Kode, U., Tinnes, J., Seefeld, B., 2017. Prochlo: Strong privacy for analytics in the crowd. In: Proc. Symposium on Operating Systems Principles. pp. 441–459
2017
Cited alongside, same era.
Differential Privacy Team, Apple, December 2017. Learning with privacy at scale
2017
Cited alongside, same era.
Ding, B., Kulkarni, J., Yekhanin, S., 2017. Collecting telemetry data privately. In: Advances in Neural Information Processing Systems. pp. 3571–3580
2017
Cited alongside, same era.
2018
Later among the works it cites.
Feng, W., Yan, Z., Zhang, H., Zeng, K., Xiao, Y., Hou, Y. T., 2018. A survey on security, privacy, and trust in mobile crowdsourcing. IEEE Internet Things J. 5 (4), 2971–2992
2018
Later among the works it cites.
2018
Later among the works it cites.
Gong, Y., Zhang, C., Fang, Y., Sun, J., 2018. Protecting location privacy for task allocation in ad hoc mobile cloud computing. IEEE Trans. Emerg. Topics Comput. 6 (1), 110–121
2018
Later among the works it cites.
2018
Later among the works it cites.
Shin, H., Kim, S., Shin, J., Xiao, X., 2018. Privacy enhanced matrix factorization for recommendation with local differential privacy. IEEE Trans. Knowl. Data Eng. 30 (9), 1770–1782
2018
Later among the works it cites.
Zhang, Z., Wang, T., Li, N., He, S., Chen, J., 2018. CALM: Consistent adaptive local marginal for marginal release under local differential privacy. In: Proc. ACM SIGSAC CCS. pp. 212–229
2018
Later among the works it cites.
Han, K., Liu, H., Tang, S., Xiao, M., Luo, J., 2019. Differentially private mechanisms for budget limited mobile crowdsourcing. IEEE Trans. Mobile Comput. 18 (4), 934–946
2019
Closest in time.
Tang, W., Zhang, K., Ren, J., Zhang, Y., Shen, X. S., 2019. Privacy-preserving task recommendation with win-win incentives for mobile crowdsourcing. Information Sciences
2019
Closest in time.
Xu, C., Ren, J., Zhang, D., Zhang, Y., Qin, Z., Ren, K., 2019. GANobfuscator: Mitigating information leakage under GAN via differential privacy. IEEE Trans. Inf. Forensics Security
2019
Closest in time.
Ye, Q., Hu, H., Meng, X., Zheng, H., 2019. PrivKV: Key-Value data collection with local differential privacy. In: IEEE Symposium on Security and Privacy
2019
Closest in time.
Jin, H., Su, L., Xiao, H., Nahrstedt, K., 2018. Incentive mechanism for privacy-aware data aggregation in mobile crowd sensing systems. IEEE/ACM Trans. Netw. 26 (5), 2019–2032
2032
Closest in time.