International Association
for Cryptologic Research

The idea of hybrid homomorphic encryption (HHE) is to drastically reduce bandwidth requirements when using homomorphic encryption (HE) at the cost of more expensive computations in the encrypted domain. To this end, various dedicated schemes for symmetric encryption have already been proposed. However, it is still unclear if those ideas are already practically useful, because (1) no cost-benefit analysis was done for use cases and (2) very few implementations are publicly available. We address this situation in several ways. We build an open-source benchmarking r framework, we explore properties of the respective HHE proposals. It turns out that even medium-sized use cases are infeasible, especially when involving integer arithmetic. Next, we propose Pasta, a cipher thoroughly optimized for integer HHE use cases. Pasta is designed to minimize the multiplicative depth, while also leveraging the structure of two state-of-the-art integer HE schemes (BFV and BGV) to minimize the homomorphic evaluation latency. Using our new benchmarking environment, we extensively evaluate Pasta in SEAL and HElib and compare its properties to 8 existing ciphers in two use cases. Our evaluations show that Pasta outperforms its competitors for HHE both in terms of homomorphic evaluation time and noise consumption, showing its efficiency for applications in real-world HE use cases. Concretely, Pasta outperforms Agrasta by a factor of up to 82, Masta by a factor of up to 6 and Hera up to a factor of 11 when applied to the two use cases.

Human mobility is undisputedly one of the critical factors in infectious disease dynamics. Until a few years ago, researchers had to rely on static data to model human mobility, which was then combined with a transmission model of a particular disease resulting in an epidemiological model. Recent works have consistently been showing that substituting the static mobility data with mobile phone data leads to significantly more accurate models. While prior studies have exclusively relied on a mobile operator’s subscribers’ aggregated data, it may be preferable to contemplate aggregated mobility data of infected individuals only. Clearly, naively linking mobile phone data with infected individuals would massively intrude privacy. This research aims to develop a software solution that reports the aggregated mobile phone location data of infected individuals while still maintaining compliance with privacy expectations. To achieve privacy, we use homomorphic encryption, zero-knowledge proof techniques, and differential privacy. Our protocol’s open-source implementation can process eight million subscribers in one hour.