A Trust-based Recruitment Framework for Multi-hop Social Participatory Sensing

The idea of social participatory sensing provides a substrate to benefit from friendship relations in recruiting a critical mass of participants willing to attend in a sensing campaign. However, the selection of suitable participants who are trustable and provide high quality contributions is challenging. In this paper, we propose a recruitment framework for social participatory sensing. Our framework leverages multi-hop friendship relations to identify and select suitable and trustworthy participants among friends or friends of friends, and finds the most trustable paths to them. The framework also includes a suggestion component which provides a cluster of suggested friends along with the path to them, which can be further used for recruitment or friendship establishment. Simulation results demonstrate the efficacy of our proposed recruitment framework in terms of selecting a large number of well-suited participants and providing contributions with high overall trust, in comparison with one-hop recruitment architecture.Comment: accepted in DCOSS 201

A Congenial Access Control Technique for Knowledge Management Systems

Usability is of extreme importance in any system design. In knowledge management systems, the need for usability is heightened by the inertia to use the system by workers. The current popular access control technique used by in KMS and portals is not exactly suitable for such a sensitive system because it does not amend to the fuzzy nature of a KMS and KM functions and ends up making the system difficult to use and violates the overall objective of the system. The research highlights usability issues as one of the problems of KMS and a potent cause of failure it was therefore treated with such seriousness. A more congenial access control technique was proposed which allows for the fuzziness inherent in KMS for large organizations. The model was evaluated through a real-world implementation – the dotCSC and the design proved viable. The system had a 0% false positive and an initial 2.1% false negative rate which was quickly corrected. It eliminated the stress of continuous role engineering and modifications. The system also recorded a high level of usability based on an online survey conducted through it. Overall, we achieved adequate security and usability, a goal which has been elusive to KMS and other systems

Applying trust metrics based on user interactions to recommendation in social networks

Recommender systems have been strongly researched within the last decade. With the arising and popularization of digital social networks a new field has been opened for social recommendations. Considering the network topology, users interactions, or estimating trust between users are some of the new strategies that recommender systems can take into account in order to adapt their techniques to these new scenarios. We introduce MarkovTrust, a way to infer trust from Twitter interactions and to compute trust between distant users. MarkovTrust is based on Markov chains, which makes it simple to be implemented and computationally efficient. We study the properties of this trust metric and study its application in a recommender system of tweets.Postprint (published version

A Testbed for Comparing Trust Computation Algorithms

Trust is the expectation of a person about another person’s behavior. Trust is important for many security related decisions about, e.g., granting or revoking privileges, controlling access to sensitive resources and information, or evaluating intelligence gathered from multiple sources. More often than not, the issue is complicated even further because the person making the decision has no direct trust relationship with every single subject whose trustworthiness needs to be evaluated. So, the decision maker needs to rely on recommendations by others, and then somehow aggregate the trust related information that is collected. In this work we provide an algebraic framework in which we can describe multiple ways that trust related information can be aggregated to form a single value. We show the similarities and differences that the various so called trust computation algorithms have, and associate these with the algebraic properties of the framework that we consider

An incentive based approach to detect selfish nodes in Mobile P2P network

The growth of mobile devices led to the wide use of Mobile P2P networks. These networks are used in a wide variety of areas and hence there is lot of research in the field of mobile networks. Detecting selfish nodes is one of the research topics triggered due to the popularity of mobile P2P networks. It is necessary to detect selfish nodes in such networks to improve the efficiency of the network. In this thesis, an incentive based approach to detect selfish nodes is designed and evaluated. This approach differs from the existing work as it (i) can be used with any underlying routing protocol assuming there are no attacks due to routing protocol (ii) is able to detect selective behavior of nodes where nodes drop some packets and forward some (iii) prevents a wide variety of malicious activities or attacks by nodes in the network (iv) prevents false positives due to connectivity issues in the network. We assume the presence of some trusted nodes called Broker nodes and propose a way using which nodes in the network communicate. Each intermediate node sends a receipt to the Broker node which it uses to identify selfish nodes in the network. Each node has a currency assigned which it uses to pay others for the forwarding service. Currency of a node is changed based on the receipts sent by that node. When the currency level of a node below some threshold, it is designated as selfish node in the network. This approach is experimentally evaluated and is found to outperform some of the recent work in this area in terms of time to detect selfish nodes and overhead involved --Abstract, page iv