Using recent technologies such as Bluetooth, mobile users can share digital content (e.g., photos, videos) with other users in proximity. However, to reduce the cognitive load on mobile users, it is important that only appropriate content is stored and presented to them.
This dissertation examines the feasibility of having mobile users filter out irrelevant content by running trust models. A trust model is a piece of software that keeps track of which devices are trusted (for sending quality content) and which are not. Unfortunately, existing trust models are not fit for purpose. Specifically, they lack the ability to: (1) reason about ratings other than binary ratings in a formal way; (2) rely on the trustworthiness of stored third-party recommendations; (3) aggregate recommendations to make accurate predictions of whom to trust; and (4) reason across categories without resorting to ontologies that are shared by all users in the system.
We overcome these shortcomings by designing and evaluating algorithms and protocols with which portable devices are able automatically to maintain information about the reputability of sources of content and to learn from each other’s recommendations. More specifically, our contributions are:
1. - An algorithm that formally reasons on generic (not necessarily binary) ratings using Bayes’ theorem.
2. - A set of security protocols with which devices store ratings in (local) tamper-evident tables and are able to check the integrity of those tables through a gossiping protocol.
3. - An algorithm that arranges recommendations in a “Web of Trust” and that makes predictions of trustworthiness that are more accurate than existing approaches by using graph-based learning.
4. - An algorithm that learns the similarity between any two categories by extracting similarities between the two categories’ ratings rather than by requiring a universal ontology. It does so automatically by using Singular Value Decomposition.
We combine these algorithms and protocols and, using real-world mobility and social network data, we evaluate the effectiveness of our proposal in allowing mobile users to select reputable sources of content. We further examine the feasibility of implementing our proposal on current mobile phones by examining the storage and computational overhead it entails. We conclude that our proposal is both feasible to implement and performs better across a range of parameters than a number of current alternatives.
|Institution||University College London|