Privacy-Preserving Gaussian Process Regression -- A Modular Approach to
the Application of Homomorphic Encryption
- URL: http://arxiv.org/abs/2001.10893v1
- Date: Tue, 28 Jan 2020 11:50:36 GMT
- Title: Privacy-Preserving Gaussian Process Regression -- A Modular Approach to
the Application of Homomorphic Encryption
- Authors: Peter Fenner, Edward O. Pyzer-Knapp
- Abstract summary: Homomorphic encryption (FHE) allows data to be computed on whilst encrypted.
Some commonly used machine learning algorithms, such as Gaussian process regression, are poorly suited to FHE.
We show that a modular approach, which applies FHE to only the sensitive steps of a workflow that need protection, allows one party to make predictions on their data.
- Score: 4.1499725848998965
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Much of machine learning relies on the use of large amounts of data to train
models to make predictions. When this data comes from multiple sources, for
example when evaluation of data against a machine learning model is offered as
a service, there can be privacy issues and legal concerns over the sharing of
data. Fully homomorphic encryption (FHE) allows data to be computed on whilst
encrypted, which can provide a solution to the problem of data privacy.
However, FHE is both slow and restrictive, so existing algorithms must be
manipulated to make them work efficiently under the FHE paradigm. Some commonly
used machine learning algorithms, such as Gaussian process regression, are
poorly suited to FHE and cannot be manipulated to work both efficiently and
accurately. In this paper, we show that a modular approach, which applies FHE
to only the sensitive steps of a workflow that need protection, allows one
party to make predictions on their data using a Gaussian process regression
model built from another party's data, without either party gaining access to
the other's data, in a way which is both accurate and efficient. This
construction is, to our knowledge, the first example of an effectively
encrypted Gaussian process.
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