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.
Motivated by the significance of associated threats, a considerable amount of work has been dedicated to user-centered design of privacy and trust enhanced OSNs. Privacy and trust are similar, but separate concepts. Nissenbaum @cite_19 discussed the concept of contextual integrity'', pointing out that personal information is not simply private or public -- privacy depends on context.
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1975980841 2069714447 2119486295 2131041789
Information assurance, security, and privacy have moved from narrow topics of interest to information system designers to become critical issues of fundamental importance to society. This opens up new requirements and opportunities for novel approaches. Meeting this challenge requires to advance the theory and practice of security, privacy, and trust of Web-based applications and to provide declarative policy representation languages and ontologies together with algorithms to reason about policies. This paper summarizes an ontological approach to enhancing the Semantic Web with security. Several efforts have been made for more privacy aware Online Social Networks (OSNs) to protect personal data against various privacy threats. However, despite the relevance of these proposals, we believe there is still the lack of a conceptual model on top of which privacy tools have to be designed. Central to this model should be the concept of risk. Therefore, in this paper, we propose a risk measure for OSNs. The aim is to associate a risk level with social network users in order to provide other users with a measure of how much it might be risky, in terms of disclosure of private information, to have interactions with them. We compute risk levels based on similarity and benefit measures, by also taking into account the user risk attitudes. In particular, we adopt an active learning approach for risk estimation, where user risk attitude is learned from few required user interactions. The risk estimation process discussed in this paper has been developed into a Facebook application and tested on real data. The experiments show the effectiveness of our proposal. The advent of the Internet has made the transmission of personally identifiable information more common and often unintended by the user. As personal information becomes more accessible, individuals worry that businesses misuse the information that is collected while they are online. Organizations have tried to mitigate this concern in two ways: (1) by offering privacy policies regarding the handling and use of personal information and (2) by offering benefits such as financial gains or convenience. In this paper, we interpret these actions in the context of the information-processing theory of motivation. Information-processing theories, also known as expectancy theories in the context of motivated behavior, are built on the premise that people process information about behavior-outcome relationships. By doing so, they are forming expectations and making decisions about what behavior to choose. Using an experimental setting, we empirically validate predictions that the means to mitigate privacy concerns are associated with positive valences resulting in an increase in motivational score. In a conjoint analysis exercise, 268 participants from the United States and Singapore face trade-off situations, where an organization may only offer incomplete privacy protection or some benefits. While privacy protections (against secondary use, improper access, and error) are associated with positive valences, we also find that financial gains and convenience can significantly increase individuals' motivational score of registering with a Web site. We find that benefits-monetary reward and future convenience-significantly affect individuals' preferences over Web sites with differing privacy policies. We also quantify the value of Web site privacy protection. Among U.S. subjects, protection against errors, improper access, and secondary use of personal information is worth @math 44.62. Finally, our approach also allows us to identify three distinct segments of Internet users-privacy guardians, information sellers, and convenience seekers. While highly successful, today's online social networks (OSNs) have made a conscious decision to sacrifice privacy for availability and centralized control. Unfortunately, tradeoffs in this "walled garden" architecture naturally pit the economic interests of OSN providers against the privacy goals of OSN users, a battle that users cannot win. While some alternative OSN designs preserve user control over data, they do so by de-prioritizing issues of economic incentives and sustainability. In contrast, we believe any practical alternative to today's centralized architecture must consider incentives for providers as a key goal. In this paper, we propose a distributed OSN architecture that significantly improves user privacy while preserving economic incentives for OSN providers. We do so by using a standardized API to create a competitive provider marketplace for different components of the OSN, thus allowing users to perform their own tradeoffs between cost, performance, and privacy. We describe Polaris, a system where users leverage smartphones as a highly available identity provider and access control manager, and use application prototypes to show how it allows data monetization while limiting the visibility of any single party to users' private data.