60,221 research outputs found
SpaceSemantics: an architecture for modeling environments
The notion of modeling location is fundamental to location awareness in ubiquitous computing environments. The investigation of models and the integration with the myriad of location sensing technologies makes for a challenging discipline. Despite notable development of location models, we believe that many challenges remain unresolved. Complexity and scalability, diverse environments coupled with various sensors and managing the privacy and security of sensitive information are open issues. In this paper we discuss our previous experience combining location sensing with mobile agents and how the lessons learnt have lead to the conception of SpaceSemantics, an open architecture for modeling environments
A Clustering-based Location Privacy Protection Scheme for Pervasive Computing
In pervasive computing environments, Location- Based Services (LBSs) are
becoming increasingly important due to continuous advances in mobile networks
and positioning technologies. Nevertheless, the wide deployment of LBSs can
jeopardize the location privacy of mobile users. Consequently, providing
safeguards for location privacy of mobile users against being attacked is an
important research issue. In this paper a new scheme for safeguarding location
privacy is proposed. Our approach supports location K-anonymity for a wide
range of mobile users with their own desired anonymity levels by clustering.
The whole area of all users is divided into clusters recursively in order to
get the Minimum Bounding Rectangle (MBR). The exact location information of a
user is replaced by his MBR. Privacy analysis shows that our approach can
achieve high resilience to location privacy threats and provide more privacy
than users expect. Complexity analysis shows clusters can be adjusted in real
time as mobile users join or leave. Moreover, the clustering algorithms possess
strong robustness.Comment: The 3rd IEEE/ACM Int Conf on Cyber, Physical and Social Computing
(CPSCom), IEEE, Hangzhou, China, December 18-20, 201
Query Processing In Location-based Services
With the advances in wireless communication technology and advanced positioning systems, a variety of Location-Based Services (LBS) become available to the public. Mobile users can issue location-based queries to probe their surrounding environments. One important type of query in LBS is moving monitoring queries over mobile objects. Due to the high frequency in location updates and the expensive cost of continuous query processing, server computation capacity and wireless communication bandwidth are the two limiting factors for large-scale deployment of moving object database systems. To address both of the scalability factors, distributed computing has been considered. These schemes enable moving objects to participate as a peer in query processing to substantially reduce the demand on server computation, and wireless communications associated with location updates. In the first part of this dissertation, we propose a distributed framework to process moving monitoring queries over moving objects in a spatial network environment. In the second part of this dissertation, in order to reduce the communication cost, we leverage both on-demand data access and periodic broadcast to design a new hybrid distributed solution for moving monitoring queries in an open space environment. Location-based services make our daily life more convenient. However, to receive the services, one has to reveal his/her location and query information when issuing locationbased queries. This could lead to privacy breach if these personal information are possessed by some untrusted parties. In the third part of this dissertation, we introduce a new privacy protection measure called query l-diversity, and provide two cloaking algorithms to achieve both location kanonymity and query l-diversity to better protect user privacy. In the fourth part of this dissertation, we design a hybrid three-tier architecture to help reduce privacy exposure. In the fifth part of this dissertation, we propose to use Road Network Embedding technique to process privacy protected queries
On-device modeling of user's social context and familiar places from smartphone-embedded sensor data
Context modeling and recognition are crucial for adaptive mobile and
ubiquitous computing. Context-awareness in mobile environments relies on prompt
reactions to context changes. However, current solutions focus on limited
context information processed on centralized architectures, risking privacy
leakage and lacking personalization. On-device context modeling and recognition
are emerging research trends, addressing these concerns. Social interactions
and visited locations play significant roles in characterizing daily life
scenarios. This paper proposes an unsupervised and lightweight approach to
model the user's social context and locations directly on the mobile device.
Leveraging the ego-network model, the system extracts high-level, semantic-rich
context features from smartphone-embedded sensor data. For the social context,
the approach utilizes data on physical and cyber social interactions among
users and their devices. Regarding location, it prioritizes modeling the
familiarity degree of specific locations over raw location data, such as GPS
coordinates and proximity devices. The effectiveness of the proposed approach
is demonstrated through three sets of experiments, employing five real-world
datasets. These experiments evaluate the structure of social and location ego
networks, provide a semantic evaluation of the proposed models, and assess
mobile computing performance. Finally, the relevance of the extracted features
is showcased by the improved performance of three machine learning models in
recognizing daily-life situations. Compared to using only features related to
physical context, the proposed approach achieves a 3% improvement in AUROC, 9%
in Precision, and 5% in Recall
Using P3P in a web services-based context-aware application platform
This paper describes a proposal for a privacy control architecture to be applied in the WASP project. The WASP project aims to develop a context-aware service platform on top of 3G networks, using web services technology. The proposed privacy control architecture is based on the P3P privacy policy description standard defined by W3C. The paper identifies extensions to P3P and its associated preference expression language APPEL that are needed to operate in a context-aware environment
Localization to Enhance Security and Services in Wi-Fi Networks under Privacy Constraints
Developments of seamless mobile services are faced with two broad challenges, systems security and user privacy - access to wireless systems is highly insecure due to the lack of physical boundaries and, secondly, location based services (LBS) could be used to extract highly sensitive user information. In this paper, we describe our work on developing systems which exploit location information to enhance security and services under privacy constraints. We describe two complimentary methods which we have developed to track node location information within production University Campus Networks comprising of large numbers of users. The location data is used to enhance security and services. Specifically, we describe a method for creating geographic firewalls which allows us to restrict and enhance services to individual users within a specific containment area regardless of physical association. We also report our work on LBS development to provide visualization of spatio-temporal node distribution under privacy considerations
Empowering users to control their privacy in context-aware system through interactive consent
Context-aware systems adapt their behaviour based on the context a user is in. Since context is potentially privacy sensitive information, users should be empowered to control how much of their context they are willing to share, under what conditions and for what purpose. We propose an interactive consent mechanism that allows this. It is interactive in the sense that users are asked for consent when a request for their context information is received. Our interactive consent mechanism complements a more traditional pre-configuration approach. We describe the architecture, the implementation of our interactive consent mechanism and a use case
Privacy, security, and trust issues in smart environments
Recent advances in networking, handheld computing and sensor technologies have driven forward research towards the realisation of Mark Weiser's dream of calm and ubiquitous computing (variously called pervasive computing, ambient computing, active spaces, the disappearing computer or context-aware computing). In turn, this has led to the emergence of smart environments as one significant facet of research in this domain. A smart environment, or space, is a region of the real world that is extensively equipped with sensors, actuators and computing components [1]. In effect the smart space becomes a part of a larger information system: with all actions within the space potentially affecting the underlying computer applications, which may themselves affect the space through the actuators. Such smart environments have tremendous potential within many application areas to improve the utility of a space. Consider the potential offered by a smart environment that prolongs the time an elderly or infirm person can live an independent life or the potential offered by a smart environment that supports vicarious learning
- âŠ