A Deep Learning Approach for Privacy Preservation in Assisted Living

TL;DR

This paper introduces a deep learning-based encoding technique using LSTM Encoder-Decoder to enhance privacy preservation in Ambient Assisted Living environments, enabling controlled data access and maintaining data utility.

Contribution

It proposes a novel LSTM-based encoding method that creates customizable data views for different user access levels, improving privacy in AAL systems.

Findings

The model learns privacy operations like disclosure, deletion, and generalization.

It achieves almost perfect data recovery after encoding and decoding.

The method is effective on simulated AAL datasets.

Abstract

In the era of Internet of Things (IoT) technologies the potential for privacy invasion is becoming a major concern especially in regards to healthcare data and Ambient Assisted Living (AAL) environments. Systems that offer AAL technologies make extensive use of personal data in order to provide services that are context-aware and personalized. This makes privacy preservation a very important issue especially since the users are not always aware of the privacy risks they could face. A lot of progress has been made in the deep learning field, however, there has been lack of research on privacy preservation of sensitive personal data with the use of deep learning. In this paper we focus on a Long Short Term Memory (LSTM) Encoder-Decoder, which is a principal component of deep learning, and propose a new encoding technique that allows the creation of different AAL data views, depending on the access level of the end user and the information they require access to. The efficiency and effectiveness of the proposed method are demonstrated with experiments on a simulated AAL dataset. Qualitatively, we show that the proposed model learns privacy operations such as disclosure, deletion and generalization and can perform encoding and decoding of the data with almost perfect recovery.