Social network data analysis raises concerns about the privacy of related entities or individuals. To address this issue, organizations can publish data after simply replacing the identities of individuals with pseudonyms, leaving the overall structure of the social network unchanged. However, it has been shown that attacks based on structural identification (e.g., a walk-based attack) enable an adversary to re-identify selected individuals in an anonymized network. In this paper we explore the capacity of techniques based on random edge perturbation to thwart such attacks. We theoretically establish that any kind of structural identification attack can effectively be prevented using random edge perturbation and show that, surprisingly, important properties of the whole network, as well as of subgraphs thereof, can be accurately calculated and hence data analysis tasks performed on the perturbed data, given that the legitimate data recipient knows the perturbation probability as well. Yet we also examine ways to enhance the walk-based attack, proposing a variant we call probabilistic attack. Nevertheless, we demonstrate that such probabilistic attacks can also be prevented under sufficient perturbation. Eventually, we conduct a thorough theoretical study of the probability of success of any}structural attack as a function of the perturbation probability. Our analysis provides a powerful tool for delineating the identification risk of perturbed social network data; our extensive experiments with synthetic and real datasets confirm our expectations.

Privacy-preserving data publication has been studied intensely in the past years. Still, all existing approaches transform data values by random perturbation or generalization. In this paper, we introduce a radically different data anonymization methodology. Our proposal aims to maintain a certain amount of patterns, defined in terms of a set of properties of interest that hold for the original data. Such properties are represented as linear relationships among data points. We present an algorithm that generates a set of anonymized data that strictly preserves these properties, thus maintaining specified patterns in the data. Extensive experiments with real and synthetic data show that our algorithm is efficient, and produces anonymized data that affords high utility in several data analysis tasks while safeguarding privacy.

Context awareness is one of the key requirements for realizing the vision of ubiquitous computing. Formal representation of context information is gaining an ever increasing number of advocates. Using the Web Ontology Language (OWL) is one of the well-established practices in this regard. Nevertheless, traditional OWL reasoning and thus query answering methods are not optimized for fast changes in context data. In this paper, we present an incremental query answering method for OWL based contextual information. Our approach consists of translating the ontology schema as well as queries into rules. These rules are then used to build the Rete network which incrementally maintains the query results as changes occur in the context data. We further employ a heuristic to adaptively prune the rules which do not affect the query results. Our incremental query answering approach requires no change in existing implementations and the empirical results suggest its practicality.

In this paper, we propose a three-tier framework for Location-Aware Service Provision and Discovery (LASPD), with special emphasis on mobile service provisioning. In the first tier, the world is geographically divided into autonomous areas to facilitate local service administration and management. In the second tier, service providers of an area with adequate computing capability are organized into a structured peer-to-peer network. The third tier supports mobile service providers equipped with less capable devices such as smart phones or PDAs. These providers are allowed to delegate their services to one of the designated peer nodes of the second tier, known as "proxy". In LASPD, the location-awareness is achieved during a service discovery by exploiting the locality-preserving property of the Hilbert space filling curve. Besides, we leverage on our previously proposed rewiring mechanism to make the network navigable and self-organized. To have a better understanding of the framework characteristics, we conduct simulations to study the routing behaviors of peer nodes in different tiers as well as measuring the performance of the prototype in real-world environments.

In this paper we present the design of an application framework for context-aware Sharing Of Living Experience (SOLE) in mobile environments. With SOLE, users can share their experience of daily activities. They can specify where to store their information - on their mobile devices to have full access control over their data, or in the distributed SOLE application servers for ease of access by others. SOLE leverages on our service-oriented middleware (Coalition) to achieve context-awareness. To cater for resource constraints of mobile devices, the concept of "proxy" is introduced. As a proof of concept, we develop a prototype of SOLE for mobile Android phone users. Preliminary experiments have been carried out to test the performance of the framework.

In this paper, we propose a pragmatic approach for location-aware service organization and discovery in structured peer-to-peer networks. To achieve local administration, the geographical space is divided into autonomous “local areas”. The local areas and service providers within each area are then organized in two separate tiers using Hilbert Space Filling Curve. Furthermore, the locality-preserving property of the Hilbert curve is exploited to ease the maintenance and to make location-based range searches feasible. Besides, we adapt the Small World Model in our novel Source Sampling method to make the resulting network model navigable. As shown in preliminary experiments, our framework performs better in terms of routing efficiency, when compared with early approaches. In addition, it reduces the workload of superpeers and mitigates the negative effect of the failure of the superpeers.

Recognizing human activities from sensor readings has recently attracted much research interest in pervasive computing. This task is particularly challenging because human activities are often performed in not only a simple (i.e., sequential), but also a complex (i.e., interleaved and concurrent) manner in real life. In this paper, we propose a novel emerging patterns based approach to sequential, interleaved and concurrent activity recognition (epSICAR). We exploit emerging patterns as powerful discriminators to differentiate activities. Different from other learning-based models built upon the training dataset for complex activities, we build our activity models by mining a set of emerging patterns from the sequential activity trace only and apply these models in recognizing sequential, interleaved and concurrent activities. We conduct our empirical studies in a real smart home, and the evaluation results demonstrate that with a time slice of 15 seconds, we achieve an accuracy of 90.96% for sequential activity, 87.98% for interleaved activity and 78.58% for concurrent activity.

The growing aging population faces a number of challenges, including rising medical cost, inadequate number of medical doctors and healthcare professionals, as well as higher incidence of misdiagnosis. There is an increasing demand for a better healthcare support for the elderly and one promising solution is the development of a context-aware middleware infrastructure for pervasive health/wellness-care. This allows the accurate and timely delivery of health/medical information among the patients, doctors and healthcare workers through a widespread deployment of wireless sensor networks and mobile devices. In this paper, we present our design and implementation of such a context-aware middleware for pervasive homecare (CAMPH). The middleware offers several key-enabling system services that consist of P2P-based context query processing, context reasoning for activity recognition and context-aware service management. It can be used to support the development and deployment of various homecare services for the elderly such as patient monitoring, location-based emergency response, anomalous daily activity detection, pervasive access to medical data and social networking. We have developed a prototype of the middleware and demonstrated the concept of providing a continuing-care to an elderly with the collaborative interactions spanning multiple physical spaces: person, home, office and clinic. The results of the prototype show that our middleware approach achieves good efficiency of context query processing and good accuracy of activity recognition.

We envisage future context-aware applications will dynamically adapt their behaviors to various context data from sources in wide-area networks, such as the Internet. Facing the changing context and the sheer number of context sources, a data management system that supports effective source organization and efficient data lookup becomes crucial to the easy development of context-aware applications. In this paper, we propose the design of a new context data management system that is equipped with query processing capabilities. We encapsulate the context sources into physical spaces belonging to different context spaces and organize them as peers in semantic overlay networks. Initial evaluation results of an experimental system prototype demonstrate the effectiveness of our design.

Context-aware computing is a key paradigm of ubiquitous computing in which applications automatically adapt their operations to dynamic context data from multiple sources. Managing a number of distributed sources, a middleware that facilitates the development of context-aware applications must provide a uniform view of all these sources to the applications. Local schemas of context data from individual sources need to be matched into a set of global schemas in the middleware, upon which applications can issue context queries to acquire data. In this paper, we study this problem of schema matching for context-aware computing. We propose a multi-criteria algorithm to determine candidate attribute matches between two schemas. The algorithm adaptively adjusts the priorities of different criteria based on previous matching results to improve the efficiency and accuracy of succeeding operations. We further develop an algorithm to categorize a new local schema into one of the global schemas whenever possible via a shared attribute dictionary. Our results based on schemas from real-world websites demonstrate the good matching accuracy achieved by our algorithms.

In this paper, we introduce a novel end-to-end approach to QoS management with respect to the diagnosis of QoS violations. We first use a set of end-to-end flow traffic statistics to describe a QoS violation. Subsequently, neural network techniques are engaged to identify and differentiate QoS violations through classification of the collected statistics. Through experiments, we find that our scheme outperforms traditional rule-based methods which require clear margins of QoS parameters in asserting a QoS violation.

In this paper, we propose a novel cross-domain QoS framework for coordinated end-to-end QoS adaptation. The framework treats each autonomous QoS service on the end-to-end path as a QoS component and models its capabilities and behavior with respect to the requirements of end-to-end QoS coordination. Based on such information, the end-to-end QoS framework runtime copes with a QoS violation by selecting a suitable QoS adaptation solution among all the end-to-end candidate choices. Preliminary simulation results demonstrate the feasibility of our framework for distributed end-to-end QoS coordination.

In this paper, we propose a peer-to-peer approach to derive and obtain additional context data from low-level context data that may be spread over multiple domains in pervasive computing environments. In this system, peers are self-organized into a semantic peer- to-peer network as the underlying communication substrate. Context reasoning is done in a distributed fashion through logical reasoning according to a set of user-defined rules. Both pull and push services are supported in the system to enable message exchange during the reasoning process. We present our design concepts, and prove the effectiveness of our system through the prototype evaluation.

This paper proposes a two-tier semantic peer-to-peer network that facilitates efficient search for context information in wide-area networks. Context data with the same semantics are grouped together into a one-dimensional semantic ring space in the upper-tier network. This is achieved by applying an ontology-based semantic clustering technique and dedicating part of node identifiers to correspond to their data semantics. In the lower-tier network, peers in each semantic cluster are organized as Chord identifier space. Thus, all the nodes in the same semantic cluster know which node is responsible for storing context data triples they are looking for, and context queries can be efficiently routed to those nodes. Through the simulation studies, we demonstrate the effectiveness of our proposed scheme.

Quality of service is commonly specified through either a set of pre-defined APIs or a specification language. The former approach has inherent limitations that make QoS specification heuristic, while the latter largely remains at syntax level, which is strong in representation formats, but weak in the semantic interpretation of QoS specification. To overcome these limitations, we propose here a semantic QoS specification (SQS) framework, which (1) provides a uniform framework for QoS specification, (2) offers tailored specification support for different application domains, and (3) facilitates machine processing of QoS specification. We adopt an ontology-based approach in the design of SQS and introduce two generic models for a more flexibile, accurate and expressive QoS specification. Moreover, the ontology modeling principle of SQS is sufficiently general to apply to the modeling of other QoS systems, which facilitates information sharing and negotiation among QoS entities along the end-to-end path. Our experimental results show that SQS have exhibited the above advantages for QoS specification with acceptable overhead in term of processing latency

This paper proposes a formal approach for protocol information modeling and validation leveraging on ontological techniques. We demonstrate the advantage of our approach through prototyping a protocol management system for representation of communication protocols and composition of protocol stacks. The prototype has an ontology-based model to describe meta-data of protocols and protocol stacks in a systematic way. Consequently, the retrieval of protocols and the validation of protocol stacks are realized by corresponding operations on the ontology model. Owing to the better expressiveness of RDFS, the ontology model can describe protocols with higher fidelity. Our experimental results show that the ontology-based protocol management system is operable and provides expressive knowledge modeling without compromising the performance

The widespread use of context information necessitates an efficient wide-area lookup service in pervasive computing. In this paper, we present semantic context space (SCS), a semantic overlay network that facilitates efficient search for context information in distributed environments. Peers in SCS are grouped based on the semantics of their local data and self-organized into a one-dimensional ring space. Context search requests are only routed to the appropriate semantic clusters, reducing unnecessary search cost on peers that have irrelevant context data, and increasing the chances that the context data will be found quickly. By exploring parallelism in a semantic cluster, search request can be found quickly. Our simulation studies demonstrate the effectiveness of SCS.

As computing technology moves towards pervasive computing, many applications are beginning to make use of context information to adapt to and respond appropriately to their environments. Such a trend necessitates efficient search for context information in wide-area networks. In this paper, we propose a semantic P2P context lookup system. Peers are grouped based on the semantics of their local data which are extracted according to a set of schemas and are self-organized as a semantic overlay network. Context search requests are only routed to the appropriate nodes that have relevant data, reducing unnecessary query traffic and increasing the chances that the context data will be found quickly. To reduce maintenance overheads incurred by high-dimensional semantic overlay networks, we propose a one-dimensional ring space to construct peers and facilitate efficient query routing. Our simulation studies demonstrate the effectiveness of our proposed routing techniques.

Service discovery has been drawing much attention from researchers and practitioners. The existing service discovery systems, like SLP, Jini, UPnP and Salutation, provide basic infrastructures where services can announce their presence and users can locate these services across the network. However there are several key issues which are partially solved or have not been well addressed - such as scalability, availability, dynamics and support for multiple matching mechanisms. In this paper, we propose a design for a service locating manager (SLM) system which addresses some of these issues. The SLM system adopts a dynamic hierarchical tree structure and service aggregation for scalability, availability and dynamics, and introduces multiple matching mechanisms which contain an attribute-based and a semantic matching engine. It provides a scalable, distributed, dynamic and robust solution to establish a flexible service discovery architecture. We describe our concepts, architecture and implementation, and present a performance study for our prototype.

In this paper, we investigate fairness properties of MAC protocols in multi-hop ad-hoc networks that have identical ranges of communication, interference and carrier sensing. We propose a new MAC protocol-hybrid asynchronous time division multiple access (HATDMA). The proposed protocol contains two schemes: SI-RI hybrid scheme and ATDMA scheme. The hybrid scheme employs both sender-initiated (SI) and receiver-initiated (RI) collision avoidance mechanisms and adoptively chooses one of them to use based on the observed performance. The ATDMA scheme requires a flow just getting a successful transmission to suspend the next transmission for a period of time that is derived from the traffic load observed around the flow. Comprehensive simulations show that HATDMA can yield high aggregate throughput and at the same time achieve very good fairness.

A novel method is developed to merge and disband groups in IP multicast while retaining the original setup of their groups. It extends the semantic of joining or leaving a membership from discrete members to group members in IP networks. It has minimal impact on existing multicast trees and does not affect the operations of multicast routing protocols and the Internet group management (IGMP). It incurs lower processing overheads by maintaining the merge status of groups at the network level instead of storing the status at application level. A prototype router and testbed have been implemented successfully to demonstrate the feasibility of the proposed solution and for performance evaluation. The proposed group connection management fills the necessary gap in the existing IP multicast architecture, where the lack of graceful merging and disbanding methods present an obstacle for more creative and complex usage of group applications.

On-demand routing protocols in mobile ad hoc networks use a process called flooding to seek the route to a destination computer. This process usually incurs a very large overhead. We examine a protocol called passive cluster-based routing protocol (PCBRP). This protocol when applied on top of any of the current routing protocols provides substantial advantages in the form of reduced overhead during flooding, minimal or no setup time and formation of more stable mobile device clusters. There are however several enhancements that can be applied to this protocol to make it more efficient and provide better connectivity among the mobile nodes. Some of the assumptions made in PCBRP regarding network traffic may not be general or realistic enough for real-world network applications. We simulate our modified protocol under a number of different conditions and attempt to optimize it so that it can perform better under varying conditions of network traffic patterns and node positions.

We propose QROUTE, an integrated framework for QoS-guaranteed multicast, in which a feasible multicast tree meeting multiple QoS constraints can be constructed in a distributed fashion using local states at routers. The simplicity in the design and implementation of QROUTE has been demonstrated by prototyping a router testbed, whose measured performance is presented. Compared to other QoS-based multicast routing protocols, QROUTE is more effective because it searches all the available feasible paths in parallel so as to ensure a better chance of successful routing and a more efficient use of network resources. We also evaluate the performance of QROUTE by comparing it with other related protocols through simulations, and find that it has lower blocking probability, lower routing overhead and lower set-up latency; the resulting multicast trees of QROUTE also generally require fewer links than those of the related protocols.

This paper proposes a timed workflow model and a temporal consistency checking approach. The proposed model incorporates the duration of task, the duration of flow, and the concept of multiple time axes task execution into the conventional workflow model. It enables the modeling of global business processes.

Multi-criteria QoS routing has been regarded by researchers as a vital mechanism to support multimedia communications. Unfortunately, finding an optimal path meeting the multi-criteria is indeed a very difficult problem to solve. This paper proposes a new QoS routing algorithm which can meet multi-criteria routing constraints, is simple to implement, and is robust. In brief, our approach is a bounded flood routing with multiple constraint checking and quick pruning at routers/switches. The quick pruning mechanism releases reserved resources at nodes not belonging to the final path, as early as possible during flooding. Bounding the flooding area within a reasonable scope reduces the routing overhead. Analysis and simulation study demonstrate that the proposed algorithm is superior to a similar flood routing scheme in terms of blocking probability, overhead and connection set-up time.

In this paper, KPN (Knowledge Petri Net), a unified Petri net model for atemporal and temporal knowledge representation and reasoning, is presented. In the model, logical, causal, uncertain/imprecise, and temporal knowledge are represented in a coherent way. Based on T-invariant computation (a classical linear algebra analysis technique of Petri nets), an efficient and simple algorithm for reasoning on KPN nets is also proposed. The model is the first attempt to handle both atemporal and temporal knowledge within a Petri net framework.

Proceedings of the ACM International Multimedia Conference & Exhibition

Most general content-based image retrieval techniques use colour and texture as main retrieval indices. A recent technique uses colour pairs to model distinct object boundaries for retrieval. These techniques have been applied to overall image contents without taking into account the characteristics of individual objects. While the techniques work well for the retrieval of images with similar overall contents (including backgrounds), their accuracies are limited because they are unable to take advantage of individual object's visual characteristics, and to perform object-level retrieval. This paper looks specifically at the use of colour-pair technique for fuzzy object-level image retrieval. Three extensions are applied to the basic colour-pair technique: (a) the development of a similarity-based ranking formula for colour-pairs matching; (b) the use of segmented objects for object-level retrieval; and (c) the inclusion of perceptually similar colours for fuzzy retrieval. A computer-aided segmentation technique is developed to segment the images' contents. Experimental results indicate that the extensions have led to substantial improvements in the retrieval performance. These extensions are sufficiently general and can be applied to other content-based image retrieval techniques.