A Fast PrivacyPreserving MultiLayer Perceptron Using RingLWEBased Homomorphic Encryption Concerns about leaking privacy from data have been preventing from making good use of socalled big data, while privacypreserving data analysis would still be a promising research direction. In this paper, we propose PrivacyPreserving MultiLayer Perceptron (PPMLP) that can compute the prediction realtime using RingLWEbased homomorphic encryption. We implement the proposed PPMLP in the form of a twoparty model consisting of client and server. The former encrypts input data and receives a classification result from a server, and the latter performs prediction over encrypted data. This scheme enables a client to acquire prediction without revealing actual data contents against a server. The proposed PPMLP can make a fast prediction that requires up to 80 msec per input without a significant drop in classification accuracy compared to the convention multilayer perceptron for plaintexts.

PlaidMLHE: Acceleration of Deep Learning Kernels to Compute on Encrypted Data Machine Learning as a Service (MLaaS) is becoming a popular practice where Service Consumers, e.g., endusers, send their data to a ML Service and receive the prediction outputs. However, the emerging usage of MLaaS has raised severe privacy concerns about usersu0027 proprietary data. PrivacyPreserving Machine Learning (PPML) techniques aim to incorporate cryptographic primitives such as Homomorphic Encryption (HE) and MultiParty Computation (MPC) into ML services to address privacy concerns from a technology standpoint. Existing PPML solutions have not been widely adopted in practice due to their assumed high overhead and integration difficulty within various ML frontend frameworks as well as hardware backends. In this work, we propose PlaidMLHE, the first endtoend HE compiler for PPML inference. Leveraging the capability of DomainSpecific Languages, PlaidMLHE enables automated generation of HE kernels across diverse types of devices. We evaluate the performance of PlaidMLHE on different ML kernels and demonstrate that PlaidMLHE greatly reduces the overhead of the HE primitive compared to the existing implementations.