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In the era of Internet of Things (IoT) technologies the potential for privacyinvasion is becoming a major concern especially in regards to healthcare dataand Ambient Assisted Living (AAL) environments. Systems that offer AALtechnologies make extensive use of personal data in order to provide servicesthat are context-aware and personalized. This makes privacy preservation a veryimportant issue especially since the users are not always aware of the privacyrisks they could face. A lot of progress has been made in the deep learningfield, however, there has been lack of research on privacy preservation ofsensitive personal data with the use of deep learning. In this paper we focuson a Long Short Term Memory (LSTM) Encoder-Decoder, which is a principalcomponent of deep learning, and propose a new encoding technique that allowsthe creation of different AAL data views, depending on the access level of theend user and the information they require access to. The efficiency andeffectiveness of the proposed method are demonstrated with experiments on asimulated AAL dataset. Qualitatively, we show that the proposed model learnsprivacy operations such as disclosure, deletion and generalization and canperform encoding and decoding of the data with almost perfect recovery.

A Deep Learning Approach for Privacy Preservation in Assisted Living