Online dating is an increasingly thriving business which boasts billion-dollar revenues and attracts users in the tens of millions. Notwithstanding its popularity, online dating is not impervious to worrisome trust and privacy concerns raised by the disclosure of potentially sensitive data as well as the exposure to self-reported (and thus potentially misrepresented) information. Nonetheless, little research has, thus far, focused on how to enhance privacy and trustworthiness. In this paper, we report on a series of semi-structured interviews involving 20 participants, and show that users are significantly concerned with the veracity of online dating profiles. To address some of these concerns, we present the user-centered design of an interface, called Certifeye, which aims to bootstrap trust in online dating profiles using existing social network data. Certifeye verifies that the information users report on their online dating profile (e.g., age, relationship status, and or photos) matches that displayed on their own Facebook profile. Finally, we present the results of a 161-user Mechanical Turk study assessing whether our veracity-enhancing interface successfully reduced concerns in online dating users and find a statistically significant trust increase.
It could be argued that acts of security theater are attempts to create . By operationalizing in this manner, we can see that it is important to increase trust in a system, as well as see that failed attempts to increase trust can lead to user frustration. Another issue typical of OSN is over-sharing . When social networks do not embed privacy into their designs, users tend to over-share and make dangerous errors. For instance, @cite_14 surveyed 569 Facebook users and found that 21 and Acquisti @cite_20 crawled the profiles of Carnegie Mellon University's Facebook population in 2005, and found that 90.8 also found that most users had not changed their privacy settings from Facebook's defaults. Sharing this kind of information can be harmful, aiding an attacker in various re-identification attacks, such as guessing a user's Social Security numbers based on publicly available information @cite_0 . In summary, while prior work has focused on privacy and trust in OSNs, or analyzed misrepresentation in ODSs, our work is the first, to the best of our knowledge, to present a user-driven and user-centered design of an ODS interface that enhances trust by leveraging information that is already available in OSNs.
@cite_0 @cite_14 @cite_20
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We have seen tremendous growth in online social networks (OSNs) in recent years. These OSNs not only offer attractive means for virtual social interactions and information sharing, but also raise a number of security and privacy issues. Although OSNs allow a single user to govern access to her his data, they currently do not provide any mechanism to enforce privacy concerns over data associated with multiple users, remaining privacy violations largely unresolved and leading to the potential disclosure of information that at least one user intended to keep private. In this paper, we propose an approach to enable collaborative privacy management of shared data in OSNs. In particular, we provide a systematic mechanism to identify and resolve privacy conflicts for collaborative data sharing. Our conflict resolution indicates a tradeoff between privacy protection and data sharing by quantifying privacy risk and sharing loss. We also discuss a proof-of-concept prototype implementation of our approach as part of an application in Facebook and provide system evaluation and usability study of our methodology. Online Social Networks (OSNs) encourage users to create an online presence that reflects their offline identity. OSNs create the illusion that these online accounts correspond to the correct offline person, but in reality the OSN lacks the resources to detect impersonation. We propose that OSN users identify each other based on interaction and experience. We believe that impersonation can be thwarted by users who possess exclusive shared knowledge, secret information shared only between a pair of OSN friends. We describe existing protocols that use shared secrets to exchange public keys without revealing those secrets to attackers. We present results from a user study on Facebook to show that users do share exclusive knowledge with their Facebook friends and attackers are rarely able to guess that knowledge. Finally, we show that friend identification can be extended using a web of trust built on the OSN friend graph. Online Social Networks (OSNs) have attracted millions of active users and have become an integral part of today's web ecosystem. Unfortunately, in the wrong hands, OSNs can be used to harvest private user data, distribute malware, control botnets, perform surveillance, spread misinformation, and even influence algorithmic trading. Usually, an adversary starts off by running an infiltration campaign using hijacked or adversary-owned OSN accounts, with an objective to connect with a large number of users in the targeted OSN. In this article, we evaluate how vulnerable OSNs are to a large-scale infiltration campaign run by socialbots: bots that control OSN accounts and mimic the actions of real users. We adopted the design of a traditional web-based botnet and built a prototype of a Socialbot Network (SbN): a group of coordinated programmable socialbots. We operated our prototype on Facebook for 8weeks, and collected data about user behavior in response to a large-scale infiltration campaign. Our results show that (1) by exploiting known social behaviors of users, OSNs such as Facebook can be infiltrated with a success rate of up to 80 , (2) subject to user profile privacy settings, a successful infiltration can result in privacy breaches where even more private user data are exposed, (3) given the economics of today's underground markets, running a large-scale infiltration campaign might be profitable but is still not particularly attractive as a sustainable and independent business, (4) the security of socially-aware systems that use or integrate OSN platforms can be at risk, given the infiltration capability of an adversary in OSNs, and (5) defending against malicious socialbots raises a set of challenges that relate to web automation, online-offline identity binding, and usable security. Due to the amount of personally identiable information shared by users of online social networks (OSNs) and the often not adequately adjusted privacy settings, it is possible to identify a user’s several OSN proles. In this paper, we illustrate that third parties just have to take a small step to link OSN proles of a user and, consequently, to aggregate various pieces of information shared in several OSNs. Particularly, based on statistical results we illustrate how the often publicly available friends lists can be exploited to link several OSN proles of a single natural person. The results presented in this paper show that proles can be linked even at low cost, i.e., without complex correlation techniques or high computational power. To assess the risk of privacy leakage by prole linking, we, additionally, report how often specic pieces of information are made publicly available by users in four of the most popular OSNs. We show that users tend to publish dierent pieces of information in dierent OSNs and, thereby, demonstrate that by linking friends lists more information about a user can be gained than the user shared in a single OSN. For the study we analyzed more than 180,000 user proles and compared more than seven million pairs of proles to investigate prole linkability.