Papers by E. Papadopoulou
Traditionally, pervasive systems are designed with a focus on the individual, offering services t... more Traditionally, pervasive systems are designed with a focus on the individual, offering services that take advantage of their physical environment and provide a context-aware, personalised user experience. On the other hand, social computing is centred around the notion of a community, leveraging the information about the users and their social relationships, connecting them together often using different criteria that can range from a user's physical location and activity to personal interests and past experiences.
Abstract One of the key objectives of a pervasive computing system is to provide appropriate supp... more Abstract One of the key objectives of a pervasive computing system is to provide appropriate support to enable the user to manage the increasingly complex environment surrounding her. This includes managing the ever-increasing number of devices which can be ...
There are various critical privacy issues that need to be addressed in the majority of smart spac... more There are various critical privacy issues that need to be addressed in the majority of smart space environments. This paper elaborates on the design of a privacy protection framework for personal self-improving smart spaces (PSSs), a concept introduced by the persist project consortium. Compared to other smart spaces, such as smart homes and vehicles, this new paradigm provides a truly
Handbook of Research on Next Generation Mobile Communication Systems, 2016
For a pervasive system to function effectively in a world in which the user is surrounded by a la... more For a pervasive system to function effectively in a world in which the user is surrounded by a large number of heterogeneous computing devices and communication systems, it is essential to provide adequate support for the user. For this personalisation is the key element, both ...
ABSTRACT One of the principal objectives of a pervasive system is to assist users in managing the... more ABSTRACT One of the principal objectives of a pervasive system is to assist users in managing the increasingly complex environments surrounding them. This is becoming an important challenge as the number and variety of electronic devices that can be remotely controlled, grows steadily. The Persist project has developed a pervasive system platform that is aimed at assisting the user to manage the devices and services around them and this has been used to demonstrate the capabilities of such a system in a number of situations. It is based on the idea of a Personal Smart Space (PSS) in which the devices belonging to its owner are connected in a network that operates as a single system. This paper focuses on its use in the context of fixed smart spaces and shows how this approach can handle this type of situation and the advantages in doing so. In order to assess its ability to deal with more complex environments that might arise in the future, the Persist platform was coupled to a simulator to simulate the appropriate devices. This was used to demonstrate how the system might be used to manage the environment on the user’s behalf, and to determine what problems need to be addressed in the future. With this users can control their physical environment with far less intervention than would otherwise be the case. Some of these ideas are being further explored in another project called Societies with the aim of producing a system that combines the ideas of pervasive system behaviour with those of social networking.
ABSTRACT Social networking systems and pervasive computing are two essential paradigms for system... more ABSTRACT Social networking systems and pervasive computing are two essential paradigms for systems of the future. There has been an increasing amount of research and development done on combining location awareness with social networking. Our current research is aimed at taking this a step further and combining more general pervasive system behaviour with social networking in a fully integrated way. In order to achieve this, one of the key functionalities on which the system is based, is that of context aware personalization. However, one of the major problems with personalization lies in dealing with the changeability of user preferences, and this needs to be taken into account when choosing a strategy to handle learning of user preferences. This paper presents an approach that we have been developing, which uses two different strategies in tandem – one based on a rule-based approach, the other on a neural network with which a user can interact. The paper briefly outlines these and then describes an experiment conducted to evaluate the time required by the neural network to adapt to changes in user preferences. This is used when the two approaches produce different results, to determine which results to use. It also provides input to help determine the frequency of execution of the learning algorithm used in the rule-based approach.
2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008
Two important concepts in developing ubiquitous or pervasive computing technologies that are acce... more Two important concepts in developing ubiquitous or pervasive computing technologies that are acceptable to the end user are personalization and privacy. On the one hand it is essential to take account of user needs and preferences to personalize decision making within such a ...
2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing, 2012
ABSTRACT Many ubiquitous computing systems employ intelligent components that learn how to adapt ... more ABSTRACT Many ubiquitous computing systems employ intelligent components that learn how to adapt the user's environment on their behalf, by observing how the user has adapted such environments in the past. Such components employ monitoring and machine learning techniques to capture human behaviours and process them to extract adaptation rules (or user preferences). However, learning preferences from observations of behaviour introduces challenges that are not so compounded in other machine learning problem domains. One key issue is preparational behaviours (or pre-actions) which current preference learning solutions can struggle to handle. This paper uses pre-actions as an example discussion point and raises the question of whether preference learning solutions should take advantage of temporal data from real-world environments to improve performance. The key contribution of this paper is the introduction and analysis of a novel machine learning technique (the DIANNE) that utilises temporal data to handle user behaviour anomalies such as pre-actions.
2008 IEEE International Symposium on Parallel and Distributed Processing with Applications, 2008
Current pervasive environments should contain mechanisms, such as personalization, that adapt the... more Current pervasive environments should contain mechanisms, such as personalization, that adapt the environment to help the user meet their individual needs. However, manually creating, maintaining and utilizing a preference set is no easy task for a user, requiring continued time and effort. A more desirable approach is to implicitly build and maintain the preference set by using monitoring and learning
(1) It should adequately protect the privacy of the user. Much work has been done on the design o... more (1) It should adequately protect the privacy of the user. Much work has been done on the design of privacy aware ubiquitous systems (eg [1], [3], [4]), including analysis of end-user requirements and the approaches needed to satisfy them. Such systems should not reveal information ...
ACM Transactions on Autonomous and Adaptive Systems, 2013
ABSTRACT Personalization mechanisms often employ behavior monitoring and machine learning techniq... more ABSTRACT Personalization mechanisms often employ behavior monitoring and machine learning techniques to aid the user in the creation and management of a preference set that is used to drive the adaptation of environments and resources in line with individual user needs. This article reviews several of the personalization solutions provided to date and proposes two hypotheses: (A) an incremental machine learning approach is better suited to the preference learning problem as opposed to the commonly employed batch learning techniques, (B) temporal data related to the duration that user context states and preference settings endure is a beneficial input to a preference learning solution. These two hypotheses are the cornerstones of the Dynamic Incremental Associative Neural NEtwork (DIANNE) developed as a tailored solution to preference learning in a pervasive environment. DIANNE has been evaluated in two ways: first, by applying it to benchmark datasets to test DIANNE's performance and scalability as a machine learning solution; second, by end-users in live trials to determine the validity of the proposed hypotheses and to evaluate DIANNE's utility as a preference learning solution.
Our world is edging closer to the realisation of pervasive systems and their integration in our e... more Our world is edging closer to the realisation of pervasive systems and their integration in our everyday life. While pervasive systems are capable of offering many benefits for everyone, the amount and quality of personal information that becomes available raise concerns about maintaining user privacy and create a real need to reform existing privacy practices and provide appropriate safeguards for the user of pervasive environments.
This thesis presents the PERSOnalised Negotiation, Identity Selection and Management (PersoNISM) system; a comprehensive approach to privacy protection in pervasive environments using context aware dynamic personalisation and behaviour learning. The aim of the PersoNISM system is twofold: to provide the user with a comprehensive set of privacy protecting tools and to help them make the best use of these tools according to their privacy needs. The PersoNISM system allows users to: a) configure the terms and conditions of data disclosure through the process of privacy policy negotiation, which addresses the current “take it or leave it” approach; b) use multiple identities to interact with pervasive services to avoid the accumulation of vast amounts of personal information in a single user profile; and c) selectively disclose information based on the type of information, who requests it, under what context, for what purpose and how the information will be treated. The PersoNISM system learns user privacy preferences by monitoring the behaviour of the user and uses them to personalise and/or automate the decision making processes in order to unburden the user from manually controlling these complex mechanisms.
The PersoNISM system has been designed, implemented, demonstrated and evaluated during three EU funded projects.
Traditionally, pervasive systems are designed with a focus on the individual, offering services t... more Traditionally, pervasive systems are designed with a focus on the individual, offering services that take advantage of their physical environment and provide a context-aware, personalised user experience. On the other hand, social computing is centred around the notion of a community, leveraging the information about the users and their social relationships, connecting them together often using different criteria that can range from a user's physical location and activity to personal interests and past experiences. The SOCIETIES Integrated Project attempts to bridge these different technologies in a unified platform allowing individuals to utilise pervasive services in a community sphere. SOCIETIES aims to use community driven context awareness, preference learning and privacy protection for intelligently connecting people, communities and things. Thus, the goal of SOCIETIES is to radically improve the utility of Future Internet services by combining the benefits of pervasive systems with these of social computing. This paper provides an overview of the vision, concepts, methodology, architecture and initial evaluation results towards the accomplishment of this goal.
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Papers by E. Papadopoulou
This thesis presents the PERSOnalised Negotiation, Identity Selection and Management (PersoNISM) system; a comprehensive approach to privacy protection in pervasive environments using context aware dynamic personalisation and behaviour learning. The aim of the PersoNISM system is twofold: to provide the user with a comprehensive set of privacy protecting tools and to help them make the best use of these tools according to their privacy needs. The PersoNISM system allows users to: a) configure the terms and conditions of data disclosure through the process of privacy policy negotiation, which addresses the current “take it or leave it” approach; b) use multiple identities to interact with pervasive services to avoid the accumulation of vast amounts of personal information in a single user profile; and c) selectively disclose information based on the type of information, who requests it, under what context, for what purpose and how the information will be treated. The PersoNISM system learns user privacy preferences by monitoring the behaviour of the user and uses them to personalise and/or automate the decision making processes in order to unburden the user from manually controlling these complex mechanisms.
The PersoNISM system has been designed, implemented, demonstrated and evaluated during three EU funded projects.
This thesis presents the PERSOnalised Negotiation, Identity Selection and Management (PersoNISM) system; a comprehensive approach to privacy protection in pervasive environments using context aware dynamic personalisation and behaviour learning. The aim of the PersoNISM system is twofold: to provide the user with a comprehensive set of privacy protecting tools and to help them make the best use of these tools according to their privacy needs. The PersoNISM system allows users to: a) configure the terms and conditions of data disclosure through the process of privacy policy negotiation, which addresses the current “take it or leave it” approach; b) use multiple identities to interact with pervasive services to avoid the accumulation of vast amounts of personal information in a single user profile; and c) selectively disclose information based on the type of information, who requests it, under what context, for what purpose and how the information will be treated. The PersoNISM system learns user privacy preferences by monitoring the behaviour of the user and uses them to personalise and/or automate the decision making processes in order to unburden the user from manually controlling these complex mechanisms.
The PersoNISM system has been designed, implemented, demonstrated and evaluated during three EU funded projects.