  The goal of homomorphic encryption is to encrypt data such that another party
can operate on it without being explicitly exposed to the content of the
original data. We introduce an idea for a privacy-preserving transformation on
natural language data, inspired by homomorphic encryption. Our primary tool is
{\em obfuscation}, relying on the properties of natural language. Specifically,
a given English text is obfuscated using a neural model that aims to preserve
the syntactic relationships of the original sentence so that the obfuscated
sentence can be parsed instead of the original one. The model works at the word
level, and learns to obfuscate each word separately by changing it into a new
word that has a similar syntactic role. The text obfuscated by our model leads
to better performance on three syntactic parsers (two dependency and one
constituency parsers) in comparison to an upper-bound random substitution
baseline. More specifically, the results demonstrate that as more terms are
obfuscated (by their part of speech), the substitution upper bound
significantly degrades, while the neural model maintains a relatively high
performing parser. All of this is done without much sacrifice of privacy
compared to the random substitution upper bound. We also further analyze the
results, and discover that the substituted words have similar syntactic
properties, but different semantic content, compared to the original words.
