Felix Wu

Crawling Online Social Graphs

Extensive research has been conducted on top of online social networks (OSNs), while little attention has been paid to the data collection process. Due to the large scale of OSNs and their privacy control policies, a partial data set is often used for analysis. The data set analyzed is decided by many factors including the choice of seeds, node selection algorithms, and the sample size. These factors may introduce biases and further contaminate or even skew the results. To evaluate the impact of different factors, this paper examines the OSN graph crawling problem, where the nodes are OSN users and the edges are the links (or relationship) among these users. More specifically, by looking at various factors in the crawling process, the following problems are addressed in this paper:* Efficiency: How fast different crawlers discover nodes/links;* Sensitivity: How different OSNs and the number of protected users affect crawlers;* Bias: How major graph properties are skewed.To the best of our knowledge, our simulations on four real world online social graphs provide the first in-depth empirical answers to these questions.

Noise Injection for Search Privacy Protection

To protect user privacy in the search engine context, most current approaches, such as private information retrieval and privacy preserving data mining, require a server-side deployment, thus users have little control over their data and privacy. In this paper we propose a user-side solution within the context of keyword based search. We model the search privacy threat as an information inference problem and show how to inject noise into user queries to minimize privacy breaches. The search privacy breach is measured as the mutual information between real user queries and the diluted queries seen by search engines. We give the lower bound for the amount of noise queries required by a perfect privacy protection and provide the optimal protection given the number of noise queries. We verify our results with a special case where the number of noise queries is equal to the number of user queries. The simulation result shows that the noise given by our approach greatly reduces privacy breaches and outperforms random noise. As far as we know, this work presents the first theoretical analysis on user side noise injection for search privacy protection.

Estimating the Size of Online Social Networks

The huge size of online social networks (OSNs) makes it prohibitively expensive to precisely measure any properties which require the knowledge of the entire graph. To estimate the size of an OSN, i.e., the number of users an OSN has, this paper introduces two estimators using widely available OSN functionalities/services. The first estimator is a maximum likelihood estimator (MLE) based on uniform sampling. An O(logn) algorithm is developed to solve the estimator, which is 70 times faster than the naive linear probing algorithm in our experiments. The second estimator is based on random walkers and we generalize it to estimate other graph properties. In-depth evaluations are conducted on six real OSNs to show the bias and variance of these two estimators. Our analysis addresses the challenges and pitfalls when developing and implementing such estimators for OSNs.

Trust Is in the Eye of the Beholder

We carefully investigate humanitys intuitive understanding of trust and extract from it fundamental properties that succinctly synthesize how trust works. From this detailed characterization we propose a formal, complete and intuitive definition of trust.Using our new definition, we prove simple possibility and impossibility theorems that dispel common misconceptions, expose unexplored areas in the design of reputation systems and shed new light on the shortcomings of previous impossibility results.

Measuring message propagation and social influence on Twitter.com

Although extensive studies have been conducted on top of online social networks OSNs, it is not clear how to characterise information propagation and social influence, two types of important but not well defined social behaviour. This paper presents a measurement study of 58 M messages collected from 700 K users on Twitter.com, a popular social medium. We analyse the propagation patterns of general messages and show how breaking news Michael Jacksons death spread through Twitter. Furthermore, we evaluate different social influences by examining their stabilities, assessments, and correlations. This paper addresses the complications and challenges we encounter when measuring message propagation and social influence on OSNs. We believe that our results here provide valuable insights for future OSN research.

Read What You Trust: An Open Wiki Model Enhanced by Social Context

Wiki systems, such as Wikipedia, provide a multitude of opportunities for large-scale online knowledge collaboration. Despite Wikipedias successes with the open editing model, dissenting voices give rise to unreliable content due to conflicts amongst contributors. From our perspective, the conflict issue results from presenting the same knowledge to all readers, without regard for the importance of the underlying social context, which both reveals the bias of contributors and influences the knowledge perception of readers. Motivated by the insufficiency of the existing knowledge presentation model for Wiki systems, this paper presents Trust Wiki, a new Wiki model which leverages social context, including social background and relationship information, to present readers with personalized and credible knowledge. Our experiment shows, with reliable social context information, Trust Wiki can efficiently assign readers to their compatible editor community and present credible knowledge derived from that community. Although this new Wiki model focuses on reinforcing the neutrality policy of Wikipedia, it also casts light on the other content reliability problems in Wiki systems, such as vandalism and minority opinion suppression.

SOLA: lightweight security for access control in IEEE 802.11

The IEEE 802.11 wireless standard provides little support for secure access control. As a result, access control in IEEE 802.11 on a per packet basis requires a new and robust identity authentication protocol. The SOLA (Statistical One-Bit Lightweight Authentication) protocol is well suited in a wireless constrained environment because this protocols communication overhead is extremely low: only one bit. Furthermore, SOLA fulfills the requirements of being secure, useful, cheap, and robust. The synchronization algorithm performs very well. SOLA also makes it easy to develop a framework to detect and respond to, for instance, denial-of-service attacks or an adversary who tries to guess the identity authentication bit for successive packets.

Automated Security Configuration Management

With the explosive growth of Internet connectivity that includes not only end-hosts but also pervasive devices, secure and reliable Internet becomes a requirement for enterprises as well as home users. Although a significant effort has been made by the research community to develop defense techniques against security attacks, efficient management of security configuration has been overlooked. Network security devices, such as Firewalls, IPSec gateways, Intrusion Detection and Prevention Systems, as well as end-host access control servers, operate based on locally configured policies. Yet these policies are not necessarily independent as they interact with each other to form global end-to-end security. Managing these security policies are complex and error prone. Intraand interpolicy conflicts or inconsistency is one of the main challenges for deploying effective security. In addition, the complexity of changing, testing, and validating security configuration in real-time environment become a major hindrance for evaluating security. Last but not least, security configurations such as policy rules are mostly defined in low level abstraction and represented in isolation of each other, which makes discovering misconfiguration in large-scale network intractable. As a result, the complexity of integrated security management not only makes

Making social interactions accessible in online social networks

Online Social Networks OSNs have changed the way people use the internet. Over the past few years these platforms have helped societies to organize riots and revolutions such as the Arab Spring or the Occupying Movements. One key fact in particular is how such events and organizations spread through out the world with social interactions, though, not much research has been focused on how to efficiently access such data and furthermore, make it available to researchers. While everyone in the field of OSN research are using tools to crawl this type of networks our approach differs significantly from the other tools out there since we are getting all interactions related to every single post. In this paper we show means of developing an efficient crawler that is able to capture all social interactions on public communities on OSNs such as Facebook.

Trustworthy opportunistic sensing: A Social Computing Paradigm

In recent years, technological advances have lead to a society with communication platforms like iPhone and Kinect Xbox that are able to inject sensing presence into online social networks (OSNs). Thus, it is possible to create large-scale opportunistic networks by integrating sensors, applications and social networks and this development could also promote innovative collaborative cyber security models. In this position paper, we discuss how social informatics will play a crucial role in trustworthy pervasive computing. With regard to security, our primary computing paradigm is still about processing information content only in order to make decisions. Given the availability of both digitized social informatics and sensor content, we now have the option to examine these sources simultaneously. We refer to this new era as the Social Computing Paradigm, and we argue that it could be particularly useful in conjunction with opportunistic sensing.