Fetching the paper…
Reading the bibliography…
The problem we address is the following: how can a user employ a predictive model that is held by a third party, without compromising private information.
The generalized Weierstrass approximation theorem
Stone, M. H · 1948
Earlier work this paper cites.
On data banks and privacy homomorphisms
Rivest, Ronald L, Adleman, Len, and Dertouzos, Michael L · 1978
Earlier work this paper cites.
Secure multi-party computation problems and their applications: a review and open problems
Du, Wenliang and Atallah, Mikhail J · 2001
Earlier work this paper cites.
Model compression
Buciluǎ, Cristian, Caruana, Rich, and Niculescu-Mizil, Alexandru · 2006
Earlier work this paper cites.
Oblivious neural network computing via homomorphic encryption
Orlandi, Claudio, Piva, Alessandro, and Barni, Mauro · 2007
Earlier work this paper cites.
Differential privacy: A survey of results
Dwork, Cynthia · 2008
Earlier work this paper cites.
Fully homomorphic encryption using ideal lattices
Gentry, Craig · 2009
Earlier work this paper cites.
A statistical framework for differential privacy
Wasserman, Larry and Zhou, Shuheng · 2010
Earlier work this paper cites.
Differentially private empirical risk minimization
Chaudhuri, Kamalika, Monteleoni, Claire, and Sarwate, Anand D · 2011
Cited alongside, same era.
Can homomorphic encryption be practical?
Lauter, Kristin, Naehrig, Michael, and Vaikuntanathan, Vinod · 2011
Cited alongside, same era.
Privacy-preserving statistical estimation with optimal convergence rates
Smith, Adam · 2011
Cited alongside, same era.
Large scale distributed deep networks
Dean, Jeffrey, Corrado, Greg, Monga, Rajat, Chen, Kai, Devin, Matthieu, Mao, Mark, Senior, Andrew, Tucker, Paul, Yang, Ke, Le, Quoc V, et al · 2012
Cited alongside, same era.
Privacy aware learning
Duchi, John C, Jordan, Michael I, and Wainwright, Martin J · 2012
Cited alongside, same era.
On-the-fly multiparty computation on the cloud via multikey fully homomorphic encryption
López-Alt, Adriana, Tromer, Eran, and Vaikuntanathan, Vinod · 2012
Cited alongside, same era.
ML confidential: Machine learning on encrypted data
Graepel, Thore, Lauter, Kristin, and Naehrig, Michael · 2013
Later among the works it cites.
Privacy-preserving matrix factorization
Nikolaenko, Valeria, Ioannidis, Stratis, Weinsberg, Udi, Joye, Marc, Taft, Nina, and Boneh, Dan · 2013
Later among the works it cites.
Privacy-preserving ridge regression on hundreds of millions of records
Nikolaenko, Valeria, Weinsberg, Udi, Ioannidis, Stratis, Joye, Marc, Boneh, Dan, and Taft, Nina · 2013
Later among the works it cites.
Do deep nets really need to be deep?
Ba, J. and Caruana, R · 2014
Closest in time.
Private predictive analysis on encrypted medical data
Bos, Joppe W, Lauter, Kristin, and Naehrig, Michael · 2014
Closest in time.
Machine learning classification over encrypted data
Bost, Raphael, Popa, Raluca Ada, Tu, Stephen, and Goldwasser, Shafi · 2014
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Using homomorphic encryption for large scale statistical analysis
Wu, David and Haven, Jacob · 2012
Cited alongside, same era.
Deep learning with cots hpc systems
Coates, Adam, Huval, Brody, Wang, Tao, Wu, David, Catanzaro, Bryan, and Andrew, Ng · 2013
Cited alongside, same era.
Efficient fully homomorphic encryption from (standard) LWE
Brakerski, Zvika and Vaikuntanathan, Vinod · 2014
Closest in time.
Private computation on encrypted genomic data
Lauter, Kristin, López-Alt, Adriana, and Naehrig, Michael · 2014
Closest in time.