The Secure Link Prediction Problem

Link Prediction is an important and well-studied problem for social networks. Given a snapshot of a graph, the link prediction problem predicts which new interactions between members are most likely to occur in the near future. As networks grow in size, data owners are forced to store the data in remote cloud servers which reveals sensitive information about the network. The graphs are therefore stored in encrypted form. We study the link prediction problem on encrypted graphs. To the best of our knowledge, this secure link prediction problem has not been studied before. We use the number of common neighbors for prediction. We present three algorithms for the secure link prediction problem. We design prototypes of the schemes and formally prove their security. We execute our algorithms in real-life datasets.Comment: This has been accepted for publication in Advances in Mathematics of Communications (AMC) journa

Locational wireless and social media-based surveillance

The number of smartphones and tablets as well as the volume of traffic generated by these devices has been growing constantly over the past decade and this growth is predicted to continue at an increasing rate over the next five years. Numerous native features built into contemporary smart devices enable highly accurate digital fingerprinting techniques. Furthermore, software developers have been taking advantage of locational capabilities of these devices by building applications and social media services that enable convenient sharing of information tied to geographical locations. Mass online sharing resulted in a large volume of locational and personal data being publicly available for extraction. A number of researchers have used this opportunity to design and build tools for a variety of uses – both respectable and nefarious. Furthermore, due to the peculiarities of the IEEE 802.11 specification, wireless-enabled smart devices disclose a number of attributes, which can be observed via passive monitoring. These attributes coupled with the information that can be extracted using social media APIs present an opportunity for research into locational surveillance, device fingerprinting and device user identification techniques. This paper presents an in-progress research study and details the findings to date

All liaisons are dangerous when all your friends are known to us

Online Social Networks (OSNs) are used by millions of users worldwide. Academically speaking, there is little doubt about the usefulness of demographic studies conducted on OSNs and, hence, methods to label unknown users from small labeled samples are very useful. However, from the general public point of view, this can be a serious privacy concern. Thus, both topics are tackled in this paper: First, a new algorithm to perform user profiling in social networks is described, and its performance is reported and discussed. Secondly, the experiments --conducted on information usually considered sensitive-- reveal that by just publicizing one's contacts privacy is at risk and, thus, measures to minimize privacy leaks due to social graph data mining are outlined.Comment: 10 pages, 5 table

Preserving Link Privacy in Social Network Based Systems

A growing body of research leverages social network based trust relationships to improve the functionality of the system. However, these systems expose users' trust relationships, which is considered sensitive information in today's society, to an adversary. In this work, we make the following contributions. First, we propose an algorithm that perturbs the structure of a social graph in order to provide link privacy, at the cost of slight reduction in the utility of the social graph. Second we define general metrics for characterizing the utility and privacy of perturbed graphs. Third, we evaluate the utility and privacy of our proposed algorithm using real world social graphs. Finally, we demonstrate the applicability of our perturbation algorithm on a broad range of secure systems, including Sybil defenses and secure routing.Comment: 16 pages, 15 figure

How to ask sensitive questions in conservation: A review of specialized questioning techniques

Tools for social research are critical for developing an understanding of conservation problems and assessing the feasibility of conservation actions. Social surveys are an essential tool frequently applied in conservation to assess both people’s behaviour and to understand its drivers. However, little attention has been given to the weaknesses and strengths of different survey tools. When topics of conservation concern are illegal or otherwise sensitive, data collected using direct questions are likely to be affected by non-response and social desirability biases, reducing their validity. These sources of bias associated with using direct questions on sensitive topics have long been recognised in the social sciences but have been poorly considered in conservation and natural resource management. We reviewed specialized questioning techniques developed in a number of disciplines specifically for investigating sensitive topics. These methods ensure respondent anonymity, increase willingness to answer, and critically, make it impossible to directly link incriminating data to an individual. We describe each method and report their main characteristics, such as data requirements, possible data outputs, availability of evidence that they can be adapted for use in illiterate communities, and summarize their main advantages and disadvantages. Recommendations for their application in conservation are given. We suggest that the conservation toolbox should be expanded by incorporating specialized questioning techniques, developed specifically to increase response accuracy. By considering the limitations of each survey technique, we will ultimately contribute to more effective evaluations of conservation interventions and more robust policy decisions

Literature Overview - Privacy in Online Social Networks

In recent years, Online Social Networks (OSNs) have become an important\ud part of daily life for many. Users build explicit networks to represent their\ud social relationships, either existing or new. Users also often upload and share a plethora of information related to their personal lives. The potential privacy risks of such behavior are often underestimated or ignored. For example, users often disclose personal information to a larger audience than intended. Users may even post information about others without their consent. A lack of experience and awareness in users, as well as proper tools and design of the OSNs, perpetuate the situation. This paper aims to provide insight into such privacy issues and looks at OSNs, their associated privacy risks, and existing research into solutions. The final goal is to help identify the research directions for the Kindred Spirits project