Differential Privacy: an Extensive Evaluation of Open-Source Tools for eHealth Applications
Typ
Examensarbete för masterexamen
Program
Computer systems and networks (MPCSN), MSc
Publicerad
2021
Författare
Hägermalm, Anton
Slavnic, Sanjin
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Medical data is fundamentally important for improving and generating new insights
in healthcare. However, personal health data is especially sensitive and statistics
about it can leak and compromise individual integrity. In order to protect the data
of individuals, or smaller groups of individuals, formal techniques can be applied
with software to modify the data and decrease the risk of unauthorized disclosure.
One such formal method that has been receiving increasing attention in the last
couple of years is differential privacy (DP).
DP adds carefully calculated noise to a statistical query, during the training of machine
learning models, or during synthetic data generation to guarantee the privacy
of the individual participants in the dataset. However, there is a gap between fundamental
and applied research on the topic of DP. Particularly, the lack of knowledge
exists over what software tools can be leveraged in privacy application development,
and to what level of performance developers can expect from potential tools.
In this thesis, we review and evaluate a set of DP tools to gain insights into how
they perform in practice. For reasonable comparison between the DP tools’ performances,
we in the evaluation categorize the tools into three domains, namely
statistical queries, machine learning, and synthetic data release. Specifically, for
statistical queries we look at Google DP and Smartnoise, for machine learning Tensorflow
Privacy, Diffprivlib and Opacus, and for synthetic data release we look at
Smartnoise and Gretel. These considered tools are open-source real-world deployments
that are created in collaboration with and vetted by a community of engineers,
scientists and experts.
In our evaluation, we measure how the tools affect data analysis, using the metrics
of accuracy and overhead, by comparing results of differential private analysis with
analysis without privacy protection. The evaluation employs two datasets in the
generating of analysis results: Parkinson Telemonitoring and 2018 Massachusetts
Health Reform Survey. For the implementation of the evaluation, we develop a
framework where the system overhead and loss of accuracy can be quantified for
any tool and any dataset, which can be reused for further tests. The full source
code for the framework is openly released as: https://github.com/anthager/
dp-evaluation. This evaluation allows us to look into how these tools perform on
real data and how they compare. With the evaluation results, we provide guidance
for how these tools can be applied to ultimately help improve privacy for individuals.
Our work provides abundant testing results on the considered tools under varieties
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of settings, allowing the view of how the tools trade off privacy and utility under
different conditions. We gain in the evaluation some general observations and trends
via comparison on the tool performance, e.g., it shows that conducting statistical
queries and ML tasks on DP synthetic data has significantly larger impact on data
accuracy than using statistical query and ML tools on non-privacy protected data,
where DP is integrated in the tools themselves. The evaluation also reveals that it
is not trivial to configure the tools, which is highly dependent on both the data,
the tool being used, and the use case. The results from our evaluation can serve as
guidelines for the optimally configuration of the tools, e.g., how the value of can
be configured for different data sizes, and what data utility can be expected in those
circumstances. We detail a summary of our results in Section 5.1.
Beskrivning
Ämne/nyckelord
Differential Privacy , Privacy Tools , Statistical Queries , Machine Learning , Synthetic Data , eHealth