Policy resolution of shared data in online social networks

Online social networks have practically a go-to source for information divulging, social exchanges and finding new friends. The popularity of such sites is so profound that they are widely used by people belonging to different age groups and various regions. Widespread use of such sites has given rise to privacy and security issues. This paper proposes a set of rules to be incorporated to safeguard the privacy policies of related users while sharing information and other forms of media online. The proposed access control network takes into account the content sensitivity and confidence level of the accessor to resolve the conflicting privacy policies of the co-owners

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

Resolving Multi-party Privacy Conflicts in Social Media

Items shared through Social Media may affect more than one user's privacy --- e.g., photos that depict multiple users, comments that mention multiple users, events in which multiple users are invited, etc. The lack of multi-party privacy management support in current mainstream Social Media infrastructures makes users unable to appropriately control to whom these items are actually shared or not. Computational mechanisms that are able to merge the privacy preferences of multiple users into a single policy for an item can help solve this problem. However, merging multiple users' privacy preferences is not an easy task, because privacy preferences may conflict, so methods to resolve conflicts are needed. Moreover, these methods need to consider how users' would actually reach an agreement about a solution to the conflict in order to propose solutions that can be acceptable by all of the users affected by the item to be shared. Current approaches are either too demanding or only consider fixed ways of aggregating privacy preferences. In this paper, we propose the first computational mechanism to resolve conflicts for multi-party privacy management in Social Media that is able to adapt to different situations by modelling the concessions that users make to reach a solution to the conflicts. We also present results of a user study in which our proposed mechanism outperformed other existing approaches in terms of how many times each approach matched users' behaviour.Comment: Authors' version of the paper accepted for publication at IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Knowledge and Data Engineering, 201

A Collaborative Access Control Model for Shared Items in Online Social Networks

The recent emergence of online social networks (OSNs) has changed the communication behaviors of thousand of millions of users. OSNs have become significant platforms for connecting users, sharing information, and a valuable source of private and sensitive data about individuals. While OSNs insert constantly new social features to increase the interaction between users, they, unfortunately, offer primitive access control mechanisms that place the burden of privacy policy configuration solely on the holder who has shared data in her/his profile regardless of other associated users, who may have different privacy preferences. Therefore, current OSN privacy mechanisms violate the privacy of all stakeholders by giving one user full authority over another’s privacy settings, which is extremely ineffective. Based on such considerations, it is essential to develop an effective and flexible access control model for OSNs, accommodating the special administration requirements coming from multiple users having a variety of privacy policies over shared items. In order to solve the identified problems, we begin by analyzing OSN scenarios where at least two users should be involved in the access control process. Afterward, we propose collaborative access control framework that enables multiple controllers of the shared item to collaboratively specify their privacy settings and to resolve the conflicts among co-controllers with different requirements and desires. We establish our conflict resolution strategy’s rules to achieve the desired equilibrium between the privacy of online users and the utility of sharing data in OSNs. We present a policy specification scheme for collaborative access control and authorization administration. Based on these considerations, we devise algorithms to achieve a collaborative access control policy over who can access or disseminate the shared item and who cannot. In our dissertation, we also present the implementation details of a proof-of-concept prototype of our approach to demonstrate the effectiveness of such an approach. With our approach, sharing and interconnection among users in OSNs will be promoted in a more trustworthy environment