Privacy-Preserving Computing via Homomorphic Encryption: Performance, Security, and Application Analysis

In the era of IoT and big data, an enormous amount of data being generated by various sensors and handheld devices and for sectors not limited to healthcare, commerce, smart driving, smart grids, and fintech requires privacy and security. Although security can be ensured once the data is in transit or at rest, for certain application domains need to ensure privacy computations over encrypted data. Homomorphic encryption (HE) is one mechanism that allows parties to compute any arbitrary functions in an encrypted domain. Homomorphic encryption schemes have been employed in various applied sectors for privacy preservation; however, the limiting factor of these schemes is the computational and communication overhead and associated security. This chapter reviews the types of HE schemes, the application domains, and the associated costs for privacy preserving computing and discusses the underlying mathematical hardness problems, security in the classical and post quantum era, and challenges and recommendations for tradeoff in applied domains.