International Association
for Cryptologic Research

As cloud computing continues to gain widespread adoption, safeguarding the confidentiality of data entrusted to third-party cloud service providers becomes a critical concern. While traditional encryption methods offer protection for data at rest and in transit, they fall short when it comes to where it matters the most, i.e., during data processing. To address this limitation, we present HELM, a framework for privacy-preserving data processing using homomorphic encryption. HELM automatically transforms arbitrary programs expressed in a Hardware Description Language (HDL), such as Verilog, into equivalent homomorphic circuits, which can then be efficiently evaluated using encrypted inputs. HELM features two modes of encrypted evaluation: a) a gate mode that consists of standard Boolean gates, and b) a lookup table mode which significantly reduces the size of the circuit by combining multiple gates into lookup tables. Finally, HELM introduces a scheduler that enables embarrassingly parallel processing in the encrypted domain. We evaluate HELM with the ISCAS'85 and ISCAS'89 benchmark suites as well as real-world applications such as AES and image filtering. Our results outperform prior works by up to $65\times$.