Biblio

We consider the problem of protecting cloud services from simultaneous white-box and black-box attacks. Recent research in cryptographic program obfuscation considers the problem of protecting the confidentiality of programs and any secrets in them. In this model, a provable program obfuscation solution makes white-box attacks to the program not more useful than black-box attacks. Motivated by very recent results showing successful black-box attacks to machine learning programs run by cloud servers, we propose and study the approach of augmenting the program obfuscation solution model so to achieve, in at least some class of application scenarios, program confidentiality in the presence of both white-box and black-box attacks.We propose and formally define encrypted-input program obfuscation, where a key is shared between the entity obfuscating the program and the entity encrypting the program's inputs. We believe this model might be of interest in practical scenarios where cloud programs operate over encrypted data received by associated sensors (e.g., Internet of Things, Smart Grid).Under standard intractability assumptions, we show various results that are not known in the traditional cryptographic program obfuscation model; most notably: Yao's garbled circuit technique implies encrypted-input program obfuscation hiding all gates of an arbitrary polynomial circuit; and very efficient encrypted-input program obfuscation for range membership programs and a class of machine learning programs (i.e., decision trees). The performance of the latter solutions has only a small constant overhead over the equivalent unobfuscated program.

Machine Learning as a Service (MLaaS) is becoming a popular practice where Service Consumers, e.g., end-users, send their data to a ML Service and receive the prediction outputs. However, the emerging usage of MLaaS has raised severe privacy concerns about users' proprietary data. PrivacyPreserving Machine Learning (PPML) techniques aim to incorporate cryptographic primitives such as Homomorphic Encryption (HE) and Multi-Party 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 front-end frameworks as well as hardware backends. In this work, we propose PlaidML-HE, the first end-toend HE compiler for PPML inference. Leveraging the capability of Domain-Specific Languages, PlaidML-HE enables automated generation of HE kernels across diverse types of devices. We evaluate the performance of PlaidML-HE on different ML kernels and demonstrate that PlaidML-HE greatly reduces the overhead of the HE primitive compared to the existing implementations.