All-or-Nothing Private Record Linkage over Streaming Data
Loading...
Date
2022-05-24
Authors
Premkumar, John Abraham
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
The prevalence and increasing need for insights obtained from the collection of sensitive
data gives rise to the problem of protecting the privacy of this data. The collection and
storage of data can be distributed across locations and organizations, and gaining insights
can require combining knowledge from different stores. Private record linkage (PRL) is
the problem of finding approximately matching records across different databases while
maintaining the privacy of all records involved. The PRL problem in the streaming data
model is an emerging problem that tackles PRL in the context of a streaming database,
where a service provider performs the matching and learns only the result to gain further
insights. To the best of our knowledge our work is the first to address this problem.
In this work, we introduce a new cryptographic primitive, the secure approximate
equality operator that securely implements all-or-nothing disclosure for approximate matching,
which has provable security guarantees in the semi-honest security model. We show that the
new operator performs several times faster than a straightforward baseline approach using
function-hiding inner product encryption. We also showcase a protocol that implements
our new approximate equality operator to perform PRL in the streaming data model with
high accuracy and performance.
Description
Keywords
private record linkage, secure computation