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Sketching is an important tool for dealing with high-dimensional vectors that are sparse (or well-approximated by a sparse vector), especially useful in distributed, parallel, and streaming settings.
Pseudorandom generators for space-bounded computations
Nisan, N · 1990
Earlier work this paper cites.
Finding frequent items in data streams
Charikar, M., Chen, K., and Farach-Colton, M · 2004
Earlier work this paper cites.
An improved data stream summary: the count-min sketch and its applications
Cormode, G. and Muthukrishnan, S · 2005
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A · 2006
Earlier work this paper cites.
Feature hashing for large scale multitask learning
Weinberger, K., Dasgupta, A., Langford, J., Smola, A., and Attenberg, J · 2009
Earlier work this paper cites.
Pan-private algorithms via statistics on sketches
Mir, D., Muthukrishnan, S., Nikolov, A., and Wright, R. N · 2011
Earlier work this paper cites.
Optimal lower bound for differentially private multi-party aggregation
Chan, T. H., Shi, E., and Song, D · 2012
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Dwork, C. and Roth, A · 2014
Earlier work this paper cites.
Sparser Johnson-Lindenstrauss transforms
Kane, D. M. and Nelson, J · 2014
Earlier work this paper cites.
Improved concentration bounds for count-sketch
Minton, G. T. and Price, E · 2014
Earlier work this paper cites.
Local, private, efficient protocols for succinct histograms
Bassily, R. and Smith, A · 2015
Cited alongside, same era.
Concentrated differential privacy: Simplifications, extensions, and lower bounds
Bun, M. and Steinke, T · 2016
Cited alongside, same era.
Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A · 2016
Cited alongside, same era.
Efficient private statistics with succinct sketches
Melis, L., Danezis, G., and Cristofaro, E. D · 2016
Cited alongside, same era.
Learning with privacy at scale
Apple Differential Privacy Team · 2017
Cited alongside, same era.
Rényi differential privacy
Mironov, I · 2017
Cited alongside, same era.
Differentially private sparse vectors with low error, optimal space, and fast access
Aumüller, M., Lebeda, C. J., and Pagh, R · 2021
Later among the works it cites.
On the power of multiple anonymous messages: Frequency estimation and selection in the shuffle model of differential privacy
Ghazi, B., Golowich, N., Kumar, R., Pagh, R., and Velingker, A · 2021
Later among the works it cites.
Advances and open problems in federated learning
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K. A., Charles, Z., Cormode, G., Cummings, R., D’Oliveira, R. G. L., Eichner, H., Rouayheb, S. E., Evans, D., Gardner, J., Garrett, Z., Gascón, A., Ghazi, B., Gibbons, P. B., Gruteser, M., Harchaoui, Z., He, C., He, L., Huo, Z., Hutchinson, B., Hsu, J., Jaggi, M., Javidi, T., Joshi, G., Khodak, M., Konečný, J., Korolova, A., Koushanfar, F., Koyejo, S., Lepoint, T., Liu, Y., Mittal, P., Mohri, M., Nock, R., Özgür, A., Pagh, R., Qi, H., Ramage, D., Raskar, R., Raykova, M., Song, D., Song, W., Stich, S. U., Sun, Z., Suresh, A. T., Tramèr, F., Vepakomma, P., Wang, J., Xiong, L., Xu, Z., Yang, Q., Yu, F. X., Yu, H., and Zhao, S · 2021
Later among the works it cites.
Countsketches, feature hashing and the median of three
Larsen, K. G., Pagh, R., and Tětek, J · 2021
Later among the works it cites.
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Hadamard response: Estimating distributions privately, efficiently, and with little communication
Acharya, J., Sun, Z., and Zhang, H · 2019
Cited alongside, same era.
Distributed differential privacy via shuffling
Cheu, A., Smith, A., Ullman, J., Zeber, D., and Zhilyaev, M · 2019
Cited alongside, same era.
Amplification by shuffling: From local to central differential privacy via anonymity
Erlingsson, Ú., Feldman, V., Mironov, I., Raghunathan, A., Talwar, K., and Thakurta, A · 2019
Cited alongside, same era.
Practical locally private heavy hitters
Bassily, R., Nissim, K., Stemmer, U., and Thakurta, A · 2020
Cited alongside, same era.
Improved differentially private euclidean distance approximation
Stausholm, N. M · 2021
Later among the works it cites.
On the robustness of countsketch to adaptive inputs
Cohen, E., Lyu, X., Nelson, J., Sarlós, T., Shechner, M., and Stemmer, U · 2022
Closest in time.
Frequency estimation under multiparty differential privacy: One-shot and streaming
Huang, Z., Qiu, Y., Yi, K., and Cormode, G · 2022
Closest in time.
Differentially private linear sketches: Efficient implementations and applications
Zhao, F., Qiao, D., Redberg, R., Agrawal, D., Abbadi, A. E., and Wang, Y · 2022
Closest in time.
Locally differentially private sparse vector aggregation
Zhou, M., Wang, T., Chan, H., Fanti, G., and Shi, E · 2022
Closest in time.