Oana Goga

On the Reliability of Profile Matching Across Large Online Social Networks

Matching the profiles of a user across multiple online social networks brings opportunities for new services and applications as well as new insights on user online behavior, yet it raises serious privacy concerns. Prior literature has showed that it is possible to accurately match profiles, but their evaluation focused only on sampled datasets. In this paper, we study the extent to which we can reliably match profiles in practice, across real-world social networks, by exploiting public attributes, i.e., information users publicly provide about themselves. Todays social networks have hundreds of millions of users, which brings completely new challenges as a reliable matching scheme must identify the correct matching profile out of the millions of possible profiles. We first define a set of properties for profile attributes--Availability, Consistency, non-Impersonability, and Discriminability (ACID)--that are both necessary and sufficient to determine the reliability of a matching scheme. Using these properties, we propose a method to evaluate the accuracy of matching schemes in real practical cases. Our results show that the accuracy in practice is significantly lower than the one reported in prior literature. When considering entire social networks, there is a non-negligible number of profiles that belong to different users but have similar attributes, which leads to many false matches. Our paper sheds light on the limits of matching profiles in the real world and illustrates the correct methodology to evaluate matching schemes in realistic scenarios.

Speed measurements of residential internet access

The spread of residential broadband Internet access is raising the question of how to measure Internet speed. We argue that available bandwidth is a key metric of access link speed. Unfortunately, the performance of available bandwidth estimation tools has rarely been tested from hosts connected to residential networks. This paper compares the accuracy and overhead of state-of-the-art available bandwidth estimation tools from hosts connected to commercial ADSL and cable networks. Our results show that, when using default settings, some tools underestimate the available bandwidth by more than 60%. We demonstrate using controlled testbeds that this happens because current home gateways have a limited packet forwarding rate.

The Doppelgänger Bot Attack: Exploring Identity Impersonation in Online Social Networks

People have long been aware of malicious users that impersonate celebrities or launch identity theft attacks in social networks. However, beyond anecdotal evidence, there have been no in-depth studies of impersonation attacks in todays social networks. One reason for the lack of studies in this space is the absence of datasets about impersonation attacks. To this end, we propose a technique to build extensive datasets of impersonation attacks in current social networks and we gather 16,572 cases of impersonation attacks in the Twitter social network. Our analysis reveals that most identity impersonation attacks are not targeting celebrities or identity theft. Instead, we uncover a new class of impersonation attacks that clone the profiles of ordinary people on Twitter to create real-looking fake identities and use them in malicious activities such as follower fraud. We refer to these as the doppelgänger bot attacks. Our findings show (i) that identity impersonation attacks are much broader than believed and can impact any user, not just celebrities and (ii) that attackers are evolving and create real-looking accounts that are harder to detect by current systems. We also propose and evaluate methods to automatically detect impersonation attacks sooner than they are being detected in todays Twitter social network.

Characterizing end-host application performance across multiple networking environments

Users today connect to the Internet everywhere - from home, work, airports, friends homes, and more. This paper characterizes how the performance of networked applications varies across networking environments. Using data from a few dozen end-hosts, we compare the distributions of RTTs and download rates across pairs of environments. We illustrate that for most users the performance difference is statistically significant. We contrast the influence of the application mix and environmental factors on these performance differences.

Strengthening Weak Identities Through Inter-Domain Trust Transfer

On most current websites untrustworthy or spammy identities are easily created. Existing proposals to detect untrustworthy identities rely on reputation signals obtained by observing the activities of identities over time within a single site or domain; thus, there is a time lag before which websites cannot easily distinguish attackers and legitimate users. In this paper, we investigate the feasibility of leveraging information about identities that is aggregated across multiple domains to reason about their trustworthiness. Our key insight is that while honest users naturally maintain identities across multiple domains (where they have proven their trustworthiness and have acquired reputation over time), attackers are discouraged by the additional effort and costs to do the same. We propose a flexible framework to transfer trust between domains that can be implemented in todays systems without significant loss of privacy or significant implementation overheads. We demonstrate the potential for inter-domain trust assessment using extensive data collected from Pinterest, Facebook, and Twitter. Our results show that newer domains such as Pinterest can benefit by transferring trust from more established domains such as Facebook and Twitter by being able to declare more users as likely to be trustworthy much earlier on (approx. one year earlier).

On Profile Linkability despite Anonymity in Social Media Systems

A number of works have recently shown that the privacy offered by pseudonymous identities on social media systems like Twitter or Reddit is threatened by cross-site identity linking attacks. Such attacks link the identities of the same user across websites. Therefore, assessing linkability, i.e., the risk that identities are linked across different websites, remains an important open problem. In this work, we analyze whether anonymity within a single social media site can protect a user from being linked across sites. To this end, we first introduce a relative linkability measure ranking identities within a social media site by their anonymity. We show that anonymity alone is not sufficient to assess linkability risks, by evaluating this measure on a data set comprising 15 million comments gathered from the Reddit social media system. Second, we mitigate this insufficiency and present our absolute linkability measure, which in addition utilizes information about matching identities. Then, we confirm the validity of this measure on our data set. The measure is able to accurately assess the linkability risk in almost 75% of the cases and, more importantly, is shown to never underestimate the linkability risk.

Identity vs. Attribute Disclosure Risks for Users with Multiple Social Profiles

Individuals sharing data on todays social computing systems face privacy losses due to information disclosure that go much beyond the data they directly share. Indeed, it was shown that it is possible to infer additional information about a user from data shared by other users--- this type of information disclosure is called attribute disclosure. Such studies, however, were limited to a single social computing system. In reality, users have identities across several social computing systems and reveal different aspects of their lives in each. This enlarges considerably the scope of information disclosure, but also complicates its analysis. Indeed, when considering multiple social computing systems, information disclosure can be of two types: attribute disclosure or identity disclosure--- which relates to the risk of pinpointing, for a given identity in a social computing system, the identity of the same individual in another social computing system. This raises the key question: how do these two privacy risks relate to each other? In this paper, we perform the first combined study of attribute and identity disclosure risks across multiple social computing systems. We first propose a framework to quantify these risks. Our empirical evaluation on a real-world dataset from Facebook and Twitter then shows that, in some regime, there is a tradeoff between the two information disclosure risks, that is, users with a lower identity disclosure risk suffer a higher attribute disclosure risk. We investigate in depth the different parameters that impact this tradeoff.

On the impact of the flow size distribution's tail index on network performance with TCP connections

In this paper, we study the impact of the flow-size distribution on network performance in the case of a single bottle-neck with finite buffer. To tackle the case where flows are transmitted with the TCP protocol, we use real experiments and ns-2 simulations. Our preliminary results show that the distributions tail index impacts the performance in a more complex way than what is reported in existing literature. In particular, we exhibit situations where a heavier tail gives better performance for certain metrics. We argue that a main cause of our observed results is the transient behavior at the beginning of each flow.