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In many applications, multiple parties have private data regarding the same set of users but on disjoint sets of attributes, and a server wants to leverage the data to train a model.
Some methods for classification and analysis of multivariate observations
J. MacQueen et al · 1967
Earlier work this paper cites.
New directions in cryptography
W. DIFFIE and M. E. HELLMAN · 1976
Earlier work this paper cites.
Algorithm as 136: A k-means clustering algorithm
J. A. Hartigan and M. A. Wong · 1979
Earlier work this paper cites.
Probabilistic counting algorithms for data base applications
P. Flajolet and G. N. Martin · 1985
Earlier work this paper cites.
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
P. J. Rousseeuw · 1987
Earlier work this paper cites.
On approximate geometric k-clustering
J. Matoušek · 2000
Earlier work this paper cites.
Similarity estimation techniques from rounding algorithms
M. S. Charikar · 2002
Earlier work this paper cites.
Privacy-preserving k-means clustering over vertically partitioned data
J. Vaidya and C. Clifton · 2003
Earlier work this paper cites.
Privacy-preserving datamining on vertically partitioned databases
C. Dwork and K. Nissim · 2004
Earlier work this paper cites.
Efficient private matching and set intersection
M. J. Freedman, K. Nissim, and B. Pinkas · 2004
Earlier work this paper cites.
On coresets for k-means and k-median clustering
S. Har-Peled and S. Mazumdar · 2004
Earlier work this paper cites.
Practical privacy: the SuLQ framework
A. Blum, C. Dwork, F. McSherry, and K. Nissim · 2005
Earlier work this paper cites.
Privacy-preserving set operations
L. Kissner and D. Song · 2005
Earlier work this paper cites.
Privacy-preserving decision trees over vertically partitioned data
J. Vaidya and C. Clifton · 2005
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
Earlier work this paper cites.
Hyperloglog: the analysis of a near-optimal cardinality estimation algorithm
P. Flajolet, É. Fusy, O. Gandouet, and F. Meunier · 2007
Earlier work this paper cites.
Smooth sensitivity and sampling in private data analysis
K. Nissim, S. Raskhodnikova, and A. Smith · 2007
Earlier work this paper cites.
V-measure: A conditional entropy-based external cluster evaluation measure
A. Rosenberg and J. Hirschberg · 2007
Earlier work this paper cites.
Efficient protocols for set intersection and pattern matching with security against malicious and covert adversaries
C. Hazay and Y. Lindell · 2008
Earlier work this paper cites.
Np-hardness of euclidean sum-of-squares clustering
D. Aloise, A. Deshpande, P. Hansen, and P. Popat · 2009
Earlier work this paper cites.
Private coresets
D. Feldman, A. Fiat, H. Kaplan, and K. Nissim · 2009
Earlier work this paper cites.
Efficient oblivious pseudorandom function with applications to adaptive ot and secure computation of set intersection
S. Jarecki and X. Liu · 2009
Earlier work this paper cites.
Privacy-preserving svm classification on vertically partitioned data without secure multi-party computation
H. Yunhong, F. Liang, and H. Guoping · 2009
Earlier work this paper cites.
Efficient set operations in the presence of malicious adversaries
C. Hazay and K. Nissim · 2010
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Earlier work this paper cites.
Fast and private computation of cardinality of set intersection and union
E. D. Cristofaro, P. Gasti, and G. Tsudik · 2012
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Differentially private subspace clustering
Y. Wang, Y.-X. Wang, and A. Singh · 2015
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Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
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K-means clustering with distributed dimensions
H. Ding, Y. Liu, L. Huang, and J. Li · 2016
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Locating a small cluster privately
K. Nissim, U. Stemmer, and S. Vadhan · 2016
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Differentially private k-means clustering
D. Su, J. Cao, N. Li, E. Bertino, and H. Jin · 2016
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VAFL: a method of vertical asynchronous federated learning, 2020
T. Chen, X. Jin, Y. Sun, and W. Yin · 2020
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Differentially-private multi-party sketching for large-scale statistics
S. G. Choi, D. Dachman-soled, M. Kulkarni, and A. Yerukhimovich · 2020
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Differentially private clustering: Tight approximation ratios
B. Ghazi, R. Kumar, and P. Manurangsi · 2020
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Federated doubly stochastic kernel learning for vertically partitioned data
B. Gu, Z. Dang, X. Li, and H. Huang · 2020
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Differentially private two-party set operations
B. Kacsmar, B. Khurram, N. Lukas, A. Norton, M. Shafieinejad, Z. Shang, Y. Baseri, M. Sepehri, S. Oya, and F. Kerschbaum · 2020
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Privacy-preserving secure cardinality and frequency estimation
B. Kreuter, C. W. Wright, E. S. Skvortsov, R. Mirisola, and Y. Wang · 2020
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Differentially private clustering in high-dimensional euclidean spaces
M.-F. Balcan, T. Dick, Y. Liang, W. Mou, and H. Zhang · 2017
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Exposed! a survey of attacks on private data
C. Dwork, A. Smith, T. Steinke, and J. Ullman · 2017
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Back to the future: an even more nearly optimal cardinality estimation algorithm
K. J. Lang · 2017
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Communication-Efficient Learning of Deep Networks from Decentralized Data
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y. Arcas · 2017
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Rényi differential privacy
I. Mironov · 2017
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Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
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Federated forest
Y. Liu, Y. Liu, Z. Liu, Y. Liang, C. Meng, J. Zhang, and Y. Zheng · 2020
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The flajolet-martin sketch itself preserves differential privacy: Private counting with minimal space
A. Smith, S. Song, and A. Thakurta · 2020
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Hybrid differentially private federated learning on vertically partitioned data
C. Wang, J. Liang, M. Huang, B. Bai, K. Bai, and H. Li · 2020
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Federated learning with differential privacy: Algorithms and performance analysis
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The value of collaboration in convex machine learning with differential privacy
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The secret revealer: Generative model-inversion attacks against deep neural networks
Y. Zhang, R. Jia, H. Pei, W. Wang, B. Li, and D. Song · 2020
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Locally private k-means in one round
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How to make private distributed cardinality estimation practical, and get differential privacy for free
C. Hu, J. Li, Z. Liu, X. Guo, Y. Wei, X. Guang, G. Loukides, and C. Dong · 2021
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Advances and open problems in federated learning
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings, et al · 2021
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Membership inference attacks and defenses in classification models
J. Li, N. Li, and B. Ribeiro · 2021
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Federated matrix factorization with privacy guarantee
Z. Li, B. Ding, C. Zhang, N. Li, and J. Zhou · 2021
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Efficient differentially private F0 linear sketching
R. Pagh and N. M. Stausholm · 2021
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(nearly) all cardinality estimators are differentially private, 2022
C. Dickens, J. Thaler, and D. Ting · 2022
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Differentially private k-means clustering (experimental)
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Differentially private vertical federated clustering
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Webank use case
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Improving privacy-preserving vertical federated learning by efficient communication with admm
C. Xie, P.-Y. Chen, C. Zhang, and B. Li · 2022
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