Project Topics
Get Complete Project Material File(s) Now! »
Privacy in Ambient Intelligence : Basic Concepts, Technologies and Challenges
In this chapter, we are going to shed light on the basic concepts related to our thesis starting with the ubiquitous and pervasive computing concepts, moving to pointing out their relationship with the ambient intelligence research area. Then, we are going to move on to listing the main applications of the ambient intelligence, focusing, mainly, on the smart home, domestic care of the elderly, assisted living and healthcare. Then, we are going to de ne the social connectedness as a part of the ambient intelligence. So, the focus is going to be on the social robot in interaction with the elderly. This leads us to highlight the di erent ethical issues regarding this kind of application. Another area of study we are going to embark on is the privacy management in ambient intelligence environments especially considering privacy in social interactions and in mobile cloud computing. Finally, we will end up by casting light on privacy challenges in the ambient intelligence.
Ubiquitous and Pervasive Computing
Ubiquitous Computing (UbiComp) as envisioned by Weiser [1] is a computing environment that integrates technologies in all types of appliances and devices in our daily life (see Figure 2.1). It aims at making computing and communication essentially transparent to users. Indeed, invisibility is the most important aspect of UbiComp. The user is exposed to a few sets of services available to him/her and is oblivious to the complex system implementing those services. Today, UbiComp includes not only an unlimited number of mobile and distributed applications but also the use of the advances of Pervasive Computing (PerCom) to present a global computing environment [2].
Basically, the terms UbiComp and Pervasive computing are used interchangeably [3], but they are conceptually di erent [4]. Pervasive computing aims at creating a smart environment with embedded and networked computing devices [5]. It provides human users with seamless service access. Autonomous detection of application requirements and automatic service provisioning are considered as the key features of pervasive computing middleware [5]. In other words, when acquiring context from the environment, PerCom dynamically builds computing models depending on context. Consequently, PerCom aims, also, at creating ambient intelligence where networked devices embedded in the environment provide unobtrusive, continual, and reliable connectivity and also perform value-added services [6].
According to the authors in [7], [6], [2], [4],[3],[8],[5], the most important and common features of UbiComp and PerCom are : connectivity everywhere and every time, interoperability, adaptability, proactivity, natural interaction via simple interfaces, context-awareness and privacy-awareness. An important feature of pervasive systems is also the ease of use. Ubiquitous computing has been designed in order to be deployed anywhere and to be accessed by the majority of users (often non-specialists). Used devices and embedded software must be manageable and accessible for all. Consequently, users are connected everywhere and every time to PerCom systems thanks to the mobile devices. Those systems are autonomous enough to capture and analyse contextual parameters of users any time and everywhere. Interoperability is considered if the environment is heterogeneous. Ubiquitous environments include a wide variety of technologies. Thus, the di culty lies on creating an intermediate system that works like a gateway, providing opportunities to all heterogeneous physical and software equipments, to interconnect, communicate and interact. Adaptability is considered when PerCom environment provides a personalized interface according to the user context. This requires an adjustment of services and an availability of resources that correspond to user context features such as its location, current activity, preferences, etc.
To better meet users’ expectations, and even anticipate their needs proactiveness should be an essential characteristic of systems, software, and devices. This can happen at various levels including the application level, between applications, the operating system level, or the network level. A proactive middleware for pervasive computing manages communication between devices connected to the real world, anticipates user needs, and initiates actions on the user’s behalf transparently, assisting the user without distracting him from the task at hand. Thus, the deployment of the technology of natural interaction interfaces should allow handling natural and intuitive : « if it is not easy to use, it will not be used ». However, simplicity can be expected to present a major challenge especially when it comes to maintain a balance between accessibility and security.
Moreover, the main feature of UbiComp and PerCom paradigms is the context awareness. Information on the context and situation makes the environment more sensitive. Systems must be able to recognize the context and customize their services according to the user context. It is, therefore, fundamental for ubiquitous technologies to incorporate data acquisition and reasoning tools about the context. In many researches [9], [10], [11], the context is limited to person’s location and time. However, there were many e orts to determine the real meaning of context and consequently of context-aware systems. Dey, in [12], considered that « context of an entity is its physical, social, emotional, and mental (focus-of-attention) environments, location and orientation, date and time of day, other objects in the environment ». This generic de nition of context can be used in any application scenario to specify and enumerate the context’s characteristics. Hence, the majority of these researches share a common vision of context as it represents a set of information about location, time and activity of a person.
Otherwise, PerCom systems collect many sensitive data about human users such as their personal information and medical records. Hence, users should be aware of how their information are used or disclosed. Many privacy-aware mechanisms control data through security policies. Although, enforcing privacy for information requests allows users controlling their information. This approach creates an imbalance between the information that users are willing to share and the data collectors’ requirements for service delivery.
Ambient Intelligence (AmI)
The term « Ambient Intelligence (AmI) » was de ned by the European Commission in 2001. In the beginning, Advisory Groups (one of the European Commission framework programme) launched the AmI challenge [13]. Then, the European Community Information Society Technology (ISTAG) updated it in 2003 [14]. Currently, AmI is the intersection of two research areas : ubiquitous and pervasive computing. AmI has, also, a strong relationship with arti cial intelligence, wireless networking, human-computer interaction and robotics domains as depicted in the gure 2.2. AmI objective is to make human daily life better by making people’s surroundings exible and adaptive [15]. The recent de nition of AmI states that it is a digital environment that proactively, but sensibly, supports people in their daily lives [16] in a non intrusive way [17].
In [18], Aarts and Ruyter, considered AmI as an electronic system that is sensitive and responsive to the presence of users. The authors identify ve key features of AmI technologies [19]: embedded, context-aware, personalized, adaptive, and anticipatory. So as far as the de nition of embedding is concerned, Aarts et al. mean possibly small miniaturized devices that merge into the background of people activities and environments [18]. Then, AmI system can improve the user’s productivity. To do so, the user should be able to communicate with the system through an enhanced user-interface, such as voice recognition.
According to the author, in [20], the most important features are intelligence and embedding. Intelligence allows us to point out that the system is sensitive to the gathered context of user from sensors and network devices. This system is able to be adaptive and learns from the behavior of users, and eventually, recognizes and expresses emotion [20]. Consequently, intelligence is related to the context/situation awareness and the personalization. In addition, AmI features will automate many aspects of our daily life, will increase the productivity at work and even will customize our shopping experiences. There are many applications elds where AmI can be valuable for users. The following is a presentation of some of these applications coupled with a focus on the smart homes and assisted living AmI applications.
• Transportation and automotive. Cook et al. argue that transport means are valuable settings for AmI technologies [20]. Public transport can bene t from AmI technology including GPS-based spatial location, vehicle identi cation and image processing to make transport more uent and hence more e cient and safe [15].
• Education. AmI can help to improve the learning experience for the stu-dents. Education-related institutions may use technology to track students progression on their tasks, frequency of attendance to speci c places and health related issues like advising on their diet regarding their habits and the class of intakes they opted for [15].
• Healthcare. AmI technologies can bring additional bene ts by integrating wearable devices such as micro-sensors to the patients and providing further support within normal daily life. These technologies aim at making it easier for individuals to monitor and maintain their own health while enjoying lives in normal social settings.
• Ambient Assisted Living and Well-being. Support for independent living for the elderly or in other words assisted living is one of the major application areas of ambient intelligence. This is due to growth of longevity of the populations.
• Emergency management. Safety-related services like re brigades can improve the reaction to a hazard by locating the place more e ciently and also by preparing the way to reach the place in connection with street services. The prison service can also quickly locate a place where a hazard is occurring or is likely to occur and prepare better access to it for personnel security [15].
• Production-oriented places. Production-centred places like factories can self-organize according to the production/demand ratio of the goods produced. This will require careful correlation between the collection of data through sensors within the di erent sections of the production line and the pool of demands via a diagnostic system which can advise the people in charge of the system at a decision-making level [15].
• Shops, shopping, recommender systems. Sadri, in [20], looks at shops as responsive environments, with devices controlled by software agents that react to the presence of customers according to the customers’ identities and pro les. The idea is to propose an ubiquitous commerce, which brings e-commerce and AmI together, with a framework for context-dependent interaction between shops and shoppers.
Ambient Intelligence Main Applications
Smart Home
A smart home is one of the famous applications of AmI. Various names have been used to describe homes equipped with pervasive technology to provide AmI services to the inhabitants. Smart homes may be the most popular term, and other terms include aware houses, intelligent homes, integrated environments, alive, interactive, responsive homes/environments. Innovation in domestic technology has long been driven and marketed by the desire to reduce labour and improve the quality of time spent at home. This continues to be one of the motivations for the development of AmI at home. Other factors include technological advances and expectations, and an increasing trend in a way of life that blurs the boundaries between home, work, rest areas and entertainment.
In [20], Sadri de nes smart home as a home equipped with sensors and actuators of various types to monitor activities and movement, and to monitor risk situations, such as re and smoke alarms. He speci es that the sensors and actuators control household appliances (e.g. cooker and fridge), household goods (e.g., taps, bed and sofa) and temperature handling devices (e.g. air conditioning and radiators) [15]. L^e et al. [21] state that the concept of « smart home » is a subject to various de nitions and interpretations because being « smart » can imply various characteristics of a highly advanced modern home, such as being automatic, compact, innovative, convenient, self-adjusting, responsive, or functional. These authors propose a conceptual framework for smart homes characterized by having ve basic features as seen in the gure 2.3 : (1) Automation which represents the ability to accommodate automatic devices or perform automatic functions; (2) Multi-functionality that shows the ability to perform various duties or generate various outcomes; (3) Adaptability that aims to make the system able to customize (or to be customized) to meet the needs of users; (4) Interactivity that means the ability to interact with or allow for interaction among users and (5) E ciency that illustrates the ability to perform functions in a time-saving, cost-saving and convenient manner.
Table of contents :
1 Introduction
2 Privacy in Ambient Intelligence : Basic Concepts, Technologies and Challenges
2.1 Introduction
2.2 Ubiquitous and Pervasive Computing
2.3 Ambient Intelligence (AmI)
2.4 Ambient Intelligence Main Applications
2.4.1 Smart Home
2.4.2 Domestic care of the elderly, Assisted Living and Healthcare
2.5 Social Connectedness
2.5.1 Social Robotics : Social Interactions with and through Ubirobots
2.5.2 Ethical Issues in Ambient Intelligence
2.6 Privacy Management in AmI Environments
2.6.1 Privacy in Social Interactions
2.6.2 Privacy in Mobile Cloud Computing
2.7 Privacy Challenges in AmI Environments
2.8 Summary
3 Privacy Management in Ambient Intelligence : State of the Art
3.1 Introduction
3.2 Privacy Requirements Analysis
3.3 Privacy by Design
3.4 Privacy Modelling
3.4.1 Semantic Modelling
3.4.2 Model-Driven Engineering
3.4.3 Model-Driven Architecture
3.4.3.1 Unied Modelling Language
3.4.3.2 Meta-Object Facility
3.4.3.3 XML Meta-data Interchange
3.4.3.4 Proles
3.4.4 Meta-modelling and Meta-Object Facility
3.4.5 Ontology versus Model-Driven Engineering
3.5 Validation of Privacy Control Systems
3.6 User Privacy Control Approaches : State of the art
3.6.1 Privacy Policy Languages
3.6.1.1 Platform for Privacy Preferences Project
3.6.1.2 Enterprise Privacy Authorization Language (EPAL)
3.6.1.3 PRIME Policy Language
3.6.2 Privacy Policies based Access Control
3.6.2.1 Classical Access Control Models
3.6.2.2 Contextual Access Control Models
3.6.2.3 Usage Control Model (UCON)
3.6.2.4 Extensible Access Control Markup Language
3.6.3 Ontology based Privacy Policy Languages
3.6.4 Analysis Framework for Privacy-aware Systems
3.7 Conclusion and Thesis Contributions
4 Semantic Privacy Management Framework
4.1 Introduction
4.2 Semantic Framework Overview
4.2.1 Meta-model Level of Privacy Policies
4.2.2 Model Level of Privacy Policies
4.2.3 Reasoning Middleware for Privacy Management
4.2.4 Description of the Foundational Meta-models
4.2.4.1 Ontology Denition Meta-model
4.2.4.2 Business Process Denition Meta-model
4.2.4.3 Semantic Executable Platform for Mapping Privacy Policies
4.3 Privacy Meta-model for Ambient Intelligence
4.3.1 Privacy Policy Templates
4.3.2 Privacy Meta-model and the MOF
4.3.3 Privacy Meta-model Specication
4.3.3.1 Privacy Policy Core Concepts
4.3.3.2 Community Management
4.3.3.3 User Management
4.3.3.4 Context Management
4.3.4 Privacy Meta-model Overview
4.3.5 OCL Constraints Rules
4.4 Conclusion
5 Design and Implementation
5.1 Introduction
5.2 MDA Application in the Software Development
5.2.1 Computation Independent Model
5.2.2 Platform Independent Model
5.2.3 Platform Specic Model
5.2.4 Model Transformation
5.3 Human-Robot Interaction Scenario
5.4 Semantic Privacy Framework at Runtime
5.4.1 Privacy Model for HRI Scenario
5.4.2 Privacy Policy Model for Daily Living Situations
5.4.2.1 Privacy Policy in the Normal Situations of Daily Living
5.4.2.2 Privacy Policy in Emergency Situation
5.5 Description of the LISSI’s Ubiquitous Platform
5.6 Conclusion
6 Conclusion
