Isolation and Dependency Resolution of Presentation, Processing and Persistence

For business application development it is important to isolate programming efforts of the concerns: Presentation, Processing and Persistence. Development of each of these concerns has an independent thinking process and requires somewhat different programming languages and development tools. In order to isolate the concerns, we provide passages between the concerns and control the flow of execution by following essentially three rules: 1. Presentation and Processing are coroutines, 2. Processing is finished before Presentation can begin to show output, and 3. Persistence is a subsystem of Processing. We explain how these rules come to existence, and what the implications are in the thesis. By following the rules, a Turing complete Presentation capable of pulling web resource from the server interactively cannot lead a programmer to write code tangling Presentation and Processing. We analyze our design and develop systems in Web 1.0 and Web 2.0 settings. For the latter setting, the system has multiple business component/service dependencies where some of the components run in the browser and some in the server. We show that such distributed component dependency can be resolved while keeping the isolation in place

Architecture and Implementation of a Trust Model for Pervasive Applications

Collaborative effort to share resources is a significant feature of pervasive computing environments. To achieve secure service discovery and sharing, and to distinguish between malevolent and benevolent entities, trust models must be defined. It is critical to estimate a device\u27s initial trust value because of the transient nature of pervasive smart space; however, most of the prior research work on trust models for pervasive applications used the notion of constant initial trust assignment. In this paper, we design and implement a trust model called DIRT. We categorize services in different security levels and depending on the service requester\u27s context information, we calculate the initial trust value. Our trust value is assigned for each device and for each service. Our overall trust estimation for a service depends on the recommendations of the neighbouring devices, inference from other service-trust values for that device, and direct trust experience. We provide an extensive survey of related work, and we demonstrate the distinguishing features of our proposed model with respect to the existing models. We implement a healthcare-monitoring application and a location-based service prototype over DIRT. We also provide a performance analysis of the model with respect to some of its important characteristics tested in various scenarios

Design and Implementation of S-MARKS: A Secure Middleware for Pervasive Computing Applications

As portable devices have become a part of our everyday life, more people are unknowingly participating in a pervasive computing environment. People engage with not a single device for a specific purpose but many devices interacting with each other in the course of ordinary activity. With such prevalence of pervasive technology, the interaction between portable devices needs to be continuous and imperceptible to device users. Pervasive computing requires a small, scalable and robust network which relies heavily on the middleware to resolve communication and security issues. In this paper, we present the design and implementation of S-MARKS which incorporates device validation, resource discovery and a privacy module

Towards a Landmark Influence Framework to Protect Location Privacy

In this paper, we present a cloaking algorithm called DirectedCloaking to protect user\u27s location privacy. There is a location anonymizer (LA) to perform this cloaking. Once this cloaked location data is provider by LA, location based service provider (LBSP) can minimize and give relevant result-candidates for a given query. LBSP can also constrain the maximum possible candidates for a given query because of our definition of a PossibleSpace. We define, for the first time, Landmark Influence Space (LIS) and show that cloaking in LIS can give the above-mentioned performance benefit to the user

WiFi Radar: Design and Implementation of an Infrastructure-less Location Tracking System for Pervasive Environment

We design and implement a low cost, easy to deploy and lightweight location tracking solution that operates without the need for a fixed infrastructure. We call it WiFi radar, which uses the signal strength of radio transmissions to determine the position (distance and direction) of devices with near linear approximation. Notwithstanding the challenges that radio frequency signals pose for location determination, the accuracy and precision of our system is relatively high. Our application is user-friendly, customizable and shows graphical as well as list views of the located objects. To the best of our knowledge, our system is the first endeavor that implements a location tracking system without the use of any fixed infrastructure and it may be the cheapest solution built so far

ELALPS: A Framework to Eliminate Location Anonymizer from Location Privacy Systems

Countless challenges to preserving a userpsilas location privacy exist and have become more important than ever before with the proliferation of handheld devices and the pervasive use of Location-based Services. It is not possible to access Location-based services and, at the same time, to preserve privacy when the user provides his exact location information. To achieve privacy, most third party based systems use a location anonymizer to achieve k-anonymity so that the user remains indistinguishable among k-1 other requesters. In this paper, we present a novel approach called ELALPS to preserve location privacy without any intermediate location anonymizer. Our framework uses a new concept, namely, Landmark Influence Space (LIS) that proves to be efficient in location anonymization and query processing. The framework is complemented by a collaboration-based light-weight k-anonymity protocol that does not require standard cryptographic operations and trust formation among users. Evaluation shows that our system has been able to bridge the gap between privacy and performance

CCTB: Context Correlation for Trust Bootstrapping in Pervasive Environment

Handheld devices in a pervasive computing environment are prone to security as well as privacy violations, while discovering, sharing and accessing services and contents. Trust models are devised to fight against such violations and breaches. Although initial trust assignment is an important issue in evolving overall trust, a little amount of work has been done in this field so far. In pervasive smart space, similar type of contexts exhibits significant correlations to each other. However, this fact is not taken into consideration while computing the initial trust values. In this paper, we describe a new mechanism to assign initial trust: CCTB (Context Correlation for Trust Bootstrapping), which takes advantage of the presence of correlations among different contexts in a context-ontology. We evaluate the effectiveness of CCTB by simulating in two different scenarios. We show that CCTB offers better initial trust values than the other models considered here. We also implement a prototype for performance measurement using .NET Compact Framework