  Growth in research collaboration has caused an increased need for sharing of
data. However, when this data is private, there is also an increased need for
maintaining security and privacy. Secure multi-party computation enables any
function to be securely evaluated over private data without revealing any
unintended data. A number of tools and compilers have been recently developed
to support evaluation of various functionalities over private data. PICCO is
one of such compilers that transforms a general-purpose user program into its
secure distributed implementation. Here we assess performance of common
statistical programs using PICCO. Specifically, we focus on chi-squared and
standard deviation computations and optimize user programs for them to assess
performance that an informed user might expect from securely evaluating these
functions using a general-purpose compiler.
