Anonymization is an important aspect of data privacy protection, especially in the context of sensitive personal information collected through sensors. In this paper, we propose a new service-based architecture for anonymizing such data in real-time, ensuring that data is accessible to authorized users while maintaining privacy. Our architecture is based on the annotation of data at ingestion time, where privacy levels are assigned to sets of columns. The anonymization procedure is performed by compressing and encoding the data through an autoencoder model, where the encoder and decoder functions are defined as parametric functions composed of multiple hidden layers.

Real-Time Anonymization of Sensitive Personal Data Using a Service-Based Architecture / F. Giampaolo, S. Izzo, S. Siccardi, A. Polimeno, V. Bellandi, F. Piccialli - In: 2023 IEEE International Conference on Web Services (ICWS)[s.l] : IEEE, 2023. - ISBN 979-8-3503-0485-5. - pp. 701-703 (( convegno ICWS tenutosi a Chicago nel 2023 [10.1109/ICWS60048.2023.00090].

Real-Time Anonymization of Sensitive Personal Data Using a Service-Based Architecture

S. Izzo;S. Siccardi;A. Polimeno;V. Bellandi;
2023

Abstract

Anonymization is an important aspect of data privacy protection, especially in the context of sensitive personal information collected through sensors. In this paper, we propose a new service-based architecture for anonymizing such data in real-time, ensuring that data is accessible to authorized users while maintaining privacy. Our architecture is based on the annotation of data at ingestion time, where privacy levels are assigned to sets of columns. The anonymization procedure is performed by compressing and encoding the data through an autoencoder model, where the encoder and decoder functions are defined as parametric functions composed of multiple hidden layers.
Data Infrastrucure; Anonymization; Healthcare Dataset; Privacy; Autoencoder
Settore INF/01 - Informatica
2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1019928
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