CryptoSPN: Privacy-preserving Sum-Product Network Inference
- URL: http://arxiv.org/abs/2002.00801v1
- Date: Mon, 3 Feb 2020 14:49:18 GMT
- Title: CryptoSPN: Privacy-preserving Sum-Product Network Inference
- Authors: Amos Treiber and Alejandro Molina and Christian Weinert and Thomas
Schneider and Kristian Kersting
- Abstract summary: We present a framework for privacy-preserving inference of sum-product networks (SPNs)
CryptoSPN achieves highly efficient and accurate inference in the order of seconds for medium-sized SPNs.
- Score: 84.88362774693914
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: AI algorithms, and machine learning (ML) techniques in particular, are
increasingly important to individuals' lives, but have caused a range of
privacy concerns addressed by, e.g., the European GDPR. Using cryptographic
techniques, it is possible to perform inference tasks remotely on sensitive
client data in a privacy-preserving way: the server learns nothing about the
input data and the model predictions, while the client learns nothing about the
ML model (which is often considered intellectual property and might contain
traces of sensitive data). While such privacy-preserving solutions are
relatively efficient, they are mostly targeted at neural networks, can degrade
the predictive accuracy, and usually reveal the network's topology.
Furthermore, existing solutions are not readily accessible to ML experts, as
prototype implementations are not well-integrated into ML frameworks and
require extensive cryptographic knowledge.
In this paper, we present CryptoSPN, a framework for privacy-preserving
inference of sum-product networks (SPNs). SPNs are a tractable probabilistic
graphical model that allows a range of exact inference queries in linear time.
Specifically, we show how to efficiently perform SPN inference via secure
multi-party computation (SMPC) without accuracy degradation while hiding
sensitive client and training information with provable security guarantees.
Next to foundations, CryptoSPN encompasses tools to easily transform existing
SPNs into privacy-preserving executables. Our empirical results demonstrate
that CryptoSPN achieves highly efficient and accurate inference in the order of
seconds for medium-sized SPNs.
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