Secure Poisson Regression
Abstract
We introduce the first construction for secure two-party computation of Poisson regression,
which enables two parties who hold shares of the input samples to learn only the resulting
Poisson model while protecting the privacy of the inputs.
Our construction relies on new protocols for secure fixed-point exponentiation and correlated matrix multiplications. Our secure exponentiation construction avoids expensive bit
decomposition and achieves orders of magnitude improvement in both online and offline costs
over state of the art works. As a result, the dominant cost for our secure Poisson regression
are matrix multiplications with one fixed matrix. We introduce a new technique, called correlated Beaver triples, which enables many such multiplications at the cost of roughly one matrix
multiplication. This further brings down the cost of secure Poisson regression.
We implement our constructions and show their extreme efficiency. In a LAN setting, our
secure exponentiation for 20-bit fractional precision takes less than 0.07ms with a batch-size of
100,000. One iteration of secure Poisson regression on a dataset with 10, 000 samples with 1000
binary features needs about 65.82s in the offline phase, 55.14s in the online phase and 17MB
total communication. For several real datasets this translates into training that takes seconds
and only a couple of MB communication
which enables two parties who hold shares of the input samples to learn only the resulting
Poisson model while protecting the privacy of the inputs.
Our construction relies on new protocols for secure fixed-point exponentiation and correlated matrix multiplications. Our secure exponentiation construction avoids expensive bit
decomposition and achieves orders of magnitude improvement in both online and offline costs
over state of the art works. As a result, the dominant cost for our secure Poisson regression
are matrix multiplications with one fixed matrix. We introduce a new technique, called correlated Beaver triples, which enables many such multiplications at the cost of roughly one matrix
multiplication. This further brings down the cost of secure Poisson regression.
We implement our constructions and show their extreme efficiency. In a LAN setting, our
secure exponentiation for 20-bit fractional precision takes less than 0.07ms with a batch-size of
100,000. One iteration of secure Poisson regression on a dataset with 10, 000 samples with 1000
binary features needs about 65.82s in the offline phase, 55.14s in the online phase and 17MB
total communication. For several real datasets this translates into training that takes seconds
and only a couple of MB communication
Research Areas
