Privacy-preserving evaluation techniques and their application in genetic tests

We discuss several protocols that apply secure multiparty computation to privacy preserving genetic testing. We categorize methods into those using oblivious finite automata, additive homomorphic encryption, garbled circuits, and private set intersection. Through comparison of performance and security metrics, we aim to make recommendations for efficient and secure multiparty computation protocols for various genetic tests including edit distance, disease susceptibility, identity/paternity/common ancestry testing, medicine and treatment efficacy for personalized medicine, and genetic compatibility.