International Association
for Cryptologic Research

We revisit private optimization and learning from an information processing view. The main contribution of this paper is twofold. First, different from the classic cryptographic framework of operation-by-operation obfuscation, a novel private learning and inference framework via either data-dependent or random transformation on the sample domain is proposed. Second, we propose a novel security analysis framework, termed probably approximately correct (PAC) inference resistance, which bridges the information loss in data processing and prior knowledge. Through data mixing, we develop an information theoretical security amplifier with a foundation of PAC security.

We study the applications of such a framework from generalized linear regression models to modern learning techniques, such as deep learning. On the information theoretical privacy side, we compare three privacy interpretations: ambiguity, statistical indistinguishability (Differential Privacy) and PAC inference resistance, and precisely describe the information leakage of our framework. We show the advantages of this new random transform approach with respect to underlying privacy guarantees, computational efficiency and utility for fully connected neural networks.