This story was originally published by Undark and is reproduced here as part of the Climate Desk collaboration. Early one winter morning in 2020, Kurt VerCauteren discovered a cluster of dead birds in a barren field in northwest Texas. They were small birds, mostly dark-eyed juncos, but also a smattering of white-crowned sparrows. VerCauteren's team had poisoned them, inadvertently. The clues were clear, the death uncomplicated: The birds had flown in before dawn to scavenge deadly morsels of a contaminated peanut paste, left behind after a sounder of wild hogs had torn through the area in a feeding frenzy. The birds likely died within minutes of eating. "I couldn't even see the crumbs," says VerCauteren, a wildlife biologist at the US Department of Agriculture in Fort Collins, Colorado, who has spent years developing and testing pig poisons. The birds were the unintended victims of a field experiment to test a toxicant--one intended for feral pigs, but no other animals--that had been developed in Australia.

Recent advances in cryptography promise to enable secure statistical computation on encrypted data, whereby a limited set of operations can be carried out without the need to first decrypt. We review these homomorphic encryption schemes in a manner accessible to statisticians and machine learners, focusing on pertinent limitations inherent in the current state of the art. These limitations restrict the kind of statistics and machine learning algorithms which can be implemented and we review those which have been successfully applied in the literature. Finally, we document a high performance R package implementing a recent homomorphic scheme in a general framework.