Input and output privacy-preserving linear regression
IEICE TRANSACTIONS on Information and Systems, 2017•search.ieice.org
We build a privacy-preserving system of linear regression protecting both input data secrecy
and output privacy. Our system achieves those goals simultaneously via a novel
combination of homomorphic encryption and differential privacy dedicated to linear
regression and its variants (ridge, LASSO). Our system is proved scalable over cloud
servers, and its efficiency is extensively checked by careful experiments.
and output privacy. Our system achieves those goals simultaneously via a novel
combination of homomorphic encryption and differential privacy dedicated to linear
regression and its variants (ridge, LASSO). Our system is proved scalable over cloud
servers, and its efficiency is extensively checked by careful experiments.
We build a privacy-preserving system of linear regression protecting both input data secrecy and output privacy. Our system achieves those goals simultaneously via a novel combination of homomorphic encryption and differential privacy dedicated to linear regression and its variants (ridge, LASSO). Our system is proved scalable over cloud servers, and its efficiency is extensively checked by careful experiments.
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