Homomorphic Encryption (HE) is a set of powerful properties of certain
cryptosystems that allow for privacy-preserving operation over the encrypted
text. Still, HE is not widespread due to limitations in terms of efficiency and
usability. Among the challenges of HE, scheme parametrization (i.e., the
selection of appropriate parameters within the algorithms) is a relevant
multi-faced problem. First, the parametrization needs to comply with a set of
properties to guarantee the security of the underlying scheme. Second,
parametrization requires a deep understanding of the low-level primitives since
the parameters have a confronting impact on the precision, performance, and
security of the scheme. Finally, the circuit to be executed influences, and it
is influenced by, the parametrization. Thus, there is no general optimal
selection of parameters, and this selection depends on the circuit and the
scenario of the application. Currently, most of the existing HE frameworks
require cryptographers to address these considerations manually. It requires a
minimum of expertise acquired through a steep learning curve. In this paper, we
propose a unified solution for the aforementioned challenges. Concretely, we
present an expert system combining Fuzzy Logic and Linear Programming. The
Fuzzy Logic Modules receive a user selection of high-level priorities for the
security, efficiency, and performance of the cryptosystem. Based on these
preferences, the expert system generates a Linear Programming Model that
obtains optimal combinations of parameters by considering those priorities
while preserving a minimum level of security for the cryptosystem. We conduct
an extended evaluation where we show that an expert system generates optimal
parameter selections that maintain user preferences without undergoing the
inherent complexity of analyzing the circuit.

本文提出了一种基于模糊逻辑和线性规划的专家系统来解决同态加密的方案参数化问题，它可以根据用户的偏好生成最优参数组合，同时保证密码系统的最低安全性水平，经扩展验证，我们展示了专家系统可以生成最优参数选择，以维护用户的偏好，同时不需要分析电路的固有复杂性。