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Authors: Davi Grossi Hasuda and Juliana de Melo Bezerra

Affiliation: Computer Science Department, ITA, São José dos Campos, Brazil

Keyword(s): Privacy, Differential Privacy, Classification Algorithms, Accuracy, Data Analysis.

Abstract: Every day an unimaginable amount of data is collected from Internet users. All this data is essential for designing, improving and suggesting products and services. In this frenzy of capturing data, privacy is often put at risk. Therefore, there is a need for considering together capturing relevant data and preserving the privacy of each person. Differential Privacy is a method that adds noise in data in a way to keep privacy. Here we investigate Differential Privacy in practice, aiming to understand how to apply it and how it can affect data analysis. We conduct experiments with four classification techniques (including Decision Tree, Näive Bayes, MLP and SVM) by varying privacy degree in order to analyze their accuracy. Our initial results show that low noise guarantees high accuracy; larger data size is not always better in the presence of noise; and noise in the target does not necessary disrupt accuracy.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Hasuda, D. and Bezerra, J. (2021). Exploring Differential Privacy in Practice. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-509-8; ISSN 2184-4992, SciTePress, pages 877-884. DOI: 10.5220/0010440408770884

@conference{iceis21,
author={Davi Grossi Hasuda. and Juliana de Melo Bezerra.},
title={Exploring Differential Privacy in Practice},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2021},
pages={877-884},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010440408770884},
isbn={978-989-758-509-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Exploring Differential Privacy in Practice
SN - 978-989-758-509-8
IS - 2184-4992
AU - Hasuda, D.
AU - Bezerra, J.
PY - 2021
SP - 877
EP - 884
DO - 10.5220/0010440408770884
PB - SciTePress

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