Reputation and credit based incentive mechanism for data-centric message delivery in delay tolerant networks

In a Data-centric Delay Tolerant Networks (DTNs), it is essential for nodes to cooperate in message forwarding in order to enable successful delivery of a message in an opportunistic fashion with nodes having their social interests defined. In the data-centric dissemination protocol proposed here, a source annotates messages (images) with keywords, and then intermediate nodes are presented with an option of adding keyword-based annotations in order to create higher content strength messages on path toward the destination. Hence, contents like images get enriched as there is situation evolution or learned by these intermediate nodes, such as in a battlefield, or in a disaster situation. Nodes might turn selfish and not participate in relaying messages due to relative scarcity of battery and storage capacity in mobile devices. Therefore, in addition to content enrichment, an incentive mechanism is proposed in this thesis which considers factors like message quality, battery usage, level of interests, etc. for the calculation of incentives. Moreover, with the goal of preventing the nodes from turning malicious by adding inappropriate message tags in the quest of acquiring more incentive, a distributed reputation model (DRM) is developed and consolidated with the proposed incentive scheme. DRM takes into account inputs from multiple users like ratings for the relevance of annotations in the message, message quality, etc. The proposed scheme safeguards the network from congestion due to uncooperative or selfish nodes in the system. The performance evaluation shows that our approach delivers more high priority and high quality messages while reducing traffic at a slightly lower message delivery ratio compared to ChitChat --Abstract, page iv

Privacy preference mechanisms in Personal Data Storage (PDS).

In this thesis, we study frameworks for managing user's privacy when disclosing personal data with third parties from Personal Data Storage (PDS). PDS is a secure digital space which allows individuals to collect, store, and give access to third parties. So, PDS has inaugurated a substantial change to the way people can store and control their personal data, by moving from a service-centric to a user-centric model. Up to now, most of the research on PDS has focused on how to enforce user privacy preferences and how to secure data stored into the PDS. In contrast, this thesis aims at designing a Privacy-aware Personal Data Storage (P-PDS), that is, a PDS able to automatically take privacy-aware decisions on third parties access requests in accordance with user preferences. This thesis first demonstrates that semi-supervised learning can be successfully exploited to make a PDS able to automatically decide whether an access request has to be authorized or not. Furthermore, we have revised our first contribution by defining strategies able to obtain good accuracy without requiring too much effort from the user in the training phase. At this aim, we exploit active learning with semi-supervised approach so as to improve the quality of the labeled training dataset. This ables to improve the performance of learning models to predict user privacy preferences correctly. Moreover, in the second part of the thesis we study how user's contextual information play a vital role in term of taking decision of whether to share personal data with third parties. As such, consider that a service provider may provide a request for entertainment service to PDS owner during his/her office hours. In such case, PDS owner may deny this service as he/she is in office. That implies individual would like to accept/deny access requests by considering his/her contextual information. Prior studies on PDS have not considered user's contextual information so far. Moreover, prior research has shown that user privacy preferences may vary based on his/her contextual information. To address this issue, this thesis also focuses to implement a contextual privacy-aware framework for PDS (CP-PDS) which exploits contextual information to build a learning classifier that can predict user privacy preferences under various contextual scenarios. We run several experiments on a realistic dataset and exploiting groups of evaluators. The obtained results show the effectiveness of the proposed approaches

INCENTIVE DRIVEN INFORMATION SHARING IN DELAY TOLERANT MOBILE NETWORKS

Versatile remote gadgets assume vital parts in our day by day life, e.g., users often use such devices to take pictures and share with friends via opportunistic distributed connections, which however are irregular in nature, and subsequently require the store and-forward feature proposed in Delay Tolerant Networks to provide useful data sharing opportunities. Moreover, cell phones may not will to forward information things to different gadgets due to the limited resources. Thus, powerful information scattering plans should be intended to urge hubs to cooperatively share data. We propose a Multi-Receiver Incentive-Based Dissemination (MuRIS) plan that permits hubs to agreeably convey information of interest to one another via chosen paths utilizing few transmissions. This plan abuses neighborhood chronicled ways and clients intrigues data kept up by every hub. What's more, the charge and remunerating capacities joined inside of the plan empower participation among nodes such that the nodes have no incentive to launch edge insertion attacks. Besides, charge and remunerating capacities are outlined such that the picked conveyance ways imitate efficient multicast tree that results in fewer delivery hops