Secure computations on non-integer values with applications to privacy-preserving sequence analysis

In this work we describe a framework which allows to perform secure computations on non-integer values. To this end, we encode values in a way similar to floating point representation and describe protocols that allow to perform efficient secure two party computations on such encoded values. We present two approaches to realize the functionality of the framework. Both approaches come with different properties and are ready to use in various application scenarios. We implemented the framework in C++ and ran several experiments. This allows for a complexity analysis and for a comparison of the two different approaches. We further describe applications to privacy-preserving computations, which greatly benefit from the use of the new framework. In particular, we show how to run an important algorithm in the context of data analysis using Hidden Markov Models (HMM), namely the Viterbi algorithm, in a secure manner.