Privacy challenges in Ambient Intelligence systems

Abstract

Abstract. Today, privacy is a key concept. It is also one which is rapidly evolving with technological advances, and there is no consensus on a single definition for it. In fact, the concept of privacy has been defined in many different ways, ranging from the "right to be left alone" to being a "commodity" that can be bought and sold. In the same time, powerful Ambient Intelligence (AmI) systems are being developed, that deploy context-aware, personalised, adaptive and anticipatory services. In such systems personal data is vastly collected, stored, and distributed, making privacy preservation a critical issue. The humancentred focus of AmI systems has prompted the introduction of new kinds of technologies, e.g. Privacy Enhancing Technologies (PET), and methodologies, e.g. Privacy by Design (PbD), whereby privacy concerns are included in the design of the system. One particular application field, where privacy preservation is of critical importance is Ambient Assisted Living (AAL). Emerging from the continuous increase of the ageing population, AAL focuses on intelligent systems of assistance for a better, healthier and safer life in their living environment. In this paper, we first build on our previous work, in which we introduced a new tripartite categorisation of privacy as a right, an enabler, and a commodity. Second, we highlight the specific privacy issues raised in AAL. Third, we review and discuss current approaches for privacy preservation. Finally, drawing on lessons learned from AAL, we provide insights on the challenges and opportunities that lie ahead. Part of our methodology is a statistical analysis performed on the IEEE publications database. We illustrate our work with AAL scenarios elaborated in cooperation with the city of Luxembourg.

On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems

“…Both of them, however, have yielded a vast scientific production, as evidenced by the important number of surveys currently present in the related literature. Specifically concerning AmI it is possible to find a wide range of studies: (i) papers of a general nature that overview the field, introducing key concepts, common use cases, and state-of-the-art techniques [6][7][8][9][10][11]; (ii) works circumscribed only to a subset of relevant related matters, such as security and privacy [12][13][14][15], sensing, computing, and networking technologies [16,17], as well as ethical implications and social issues [18], and (iii) research focused on particular application domains (e.g., architecture [19], workplaces [20], educational environments [21,22], and smart cities [18]), with healthcare being the one that has shown greater treatment [12,[23][24][25][26][27][28][29]. For its part, the on-device object detection corpus is limited to literature published over the last five years (2017)(2018)(2019)(2020)(2021).…”

homeSound: Real-Time Audio Event Detection Based on High Performance Computing for Behaviour and Surveillance Remote Monitoring

“…Also, considering the real-time and reliability constraints imposed by this kind of applications (i.e., both the servers and the communication links need to be operating 100% of the time), the degree of redundancy and the communication protocols needed to meet these requirements would result in an expensive solution [13]. Additionally, in this approach very sensible personal data are stored in the cloud, which makes them prone to cyber attacks and, thus, would require an expensive additional layer of security [14,15]. …”

“…Then, each device only sends to the monitoring system the encrypted acoustic event identifier. This approach addresses both the burdensome communication costs and the principle of privacy [15] simultaneously in a cost effective way. Overall, this strategy can also be seen as a MEC architecture where the cloud computing infrastructure consists of a single computing node and user equipments (i.e., microphones) have no computing capabilities [17].…”

Reality and perception: Activity monitoring and data collection within a real-world smart home

“…However, the advances in AAL rely increasingly on personal data. Such data are sensitive and therefore can lead to privacy risks such as privacy of behaviour and action, privacy of communication, privacy of data and image, privacy of location, and privacy of association [2], [7]. It is also difficult to find the right balance between individual privacy and availability of data.…”