Anonymity forms an integral and important part of our digital life. It enables us to express our true selves without the fear of judgment. In this paper, we investigate the different aspects of anonymity in the social Q&A site Quora. The choice of Quora is motivated by the fact that this is one of the rare social Q&A sites that allow users to explicitly post anonymous questions and such activity in this forum has become normative rather than a taboo. Through an analysis of 5.1 million questions, we observe that at a global scale almost no difference manifests between the linguistic structure of the anonymous and the non-anonymous questions. We find that topical mixing at the global scale to be the primary reason for the absence. However, the differences start to feature once we "deep dive" and (topically) cluster the questions and compare the clusters that have high volumes of anonymous questions with those that have low volumes of anonymous questions. In particular, we observe that the choice to post the question as anonymous is dependent on the user's perception of anonymity and they often choose to speak about depression, anxiety, social ties and personal issues under the guise of anonymity. We further perform personality trait analysis and observe that the anonymous group of users has positive correlation with extraversion, agreeableness, and negative correlation with openness. Subsequently, to gain further insights, we build an anonymity grid to identify the differences in the perception on anonymity of the user posting the question and the community of users answering it. We also look into the first response time of the questions and observe that it is lowest for topics which talk about personal and sensitive issues, which hints toward a higher degree of community support and user engagement.
Another set of studies detail the positive aspects of anonymity mainly involving self-disclosure and degree of intimacy. In some research, the absence of interviewer has been found to increase the duration of self-disclosure for participants who were presented with intimate questions @cite_7 . Privacy has been shown to have positive effect on human well-being in @cite_17 . find that anonymity helps users to discuss topics which are considered to be stigma in the real world. People regularly use the protection of anonymity to reduce the social risks and to create different persona online than they exhibit offline @cite_2 @cite_1 . In , the authors find that the users use online dating services in identity exploration and recreation process. Anonymity also helps in the protection of informants (e.g., whistle-blowers or news sources).
@cite_1 @cite_2 @cite_7 @cite_17
2163598416 2047691574 2119486295 2086526348
As more of our daily activities are carried out online, it becomes important to develop technologies to protect our online privacy. Anonymity is a key privacy technology, since it serves to hide patterns of communication that can often be as revealing as their contents. This motivates our study of the use of large scale peer-to-peer systems for building anonymous systems. We first develop a novel methodology for studying the anonymity of peer-to-peer systems, based on an information-theoretic anonymity metric and simulation. We use simulations to sample a probability distribution modeling attacker knowledge under conservative assumptions and estimate the entropy-based anonymity metric using the sampled distribution. We then validate this approach against an analytic method for computing entropy. The use of sampling introduces some error, but it can be accurately bounded and therefore we can make rigorous statements about the success of an entire class of attacks. We next apply our methodology to perform the first rigorous analysis of Freenet, a peer-to-peer anonymous publishing system, and identify a number of weaknesses in its design. We show that a targeted attack on high-degree nodes can be very effective at reducing anonymity. We also consider a next generation routing algorithm proposed by the Freenet authors to improve performance and show that it has a significant negative impact on anonymity. Finally, even in the best case scenario, the anonymity levels provided by Freenet are highly variable and, in many cases, little or no anonymity is achieved. To provide more uniform anonymity protection, we propose a new design for peer-to-peer anonymous systems based on structured overlays. We use random walks along the overlay to provide anonymity. We compare the mixing times of random walks on different graph structures and find that de Bruijn graphs are superior to other structures such as the hypercube or butterfly. Using our simulation methodology, we analyze the anonymity achieved by our design running on top of Koorde, a structured overlay based on de Bruijn graphs. We show that it provides anonymity competitive with Freenet in the average case, while ensuring that worst-case anonymity remains at an acceptable level. We also maintain logarithmic guarantees on routing performance. The growth of the Internet at a means of communication has sparked the interest of researchers in several fields (e.g. communication, social psychology, industrial-organizational psychology) to investigate the issues surrounding the expression of different social behaviors in this unique social context. Of special interest to researchers is the increased importance that anonymity seems to play in computer-mediated communication (CMC). This paper reviews the literature related to the issues of anonymity within the social context, particularly in CMC, demonstrating the usefulness of established social psychological theory to explain behavior in CMC and discussing the evolution of the current theoretical explanations in explaining the effects of anonymity in social behavior in CMC environments. Several suggestions for future research are proposed in an attempt to provide researchers with new avenues to investigate how anonymity can play both positive and negative roles in CMC. 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. A common view in some data anonymization literature is to oppose the "old'' k-anonymity model to the "new'' differential privacy model, which offers more robust privacy guarantees. However, the utility of the masked results provided by differential privacy is usually limited, due to the amount of noise that needs to be added to the output, or because utility can only be guaranteed for a restricted type of queries. This is in contrast with the general-purpose anonymized data resulting from k-anonymity mechanisms, which also focus on preserving data utility. In this paper, we show that a synergy between differential privacy and k-anonymity can be found when the objective is to release anonymized data: k-anonymity can help improving the utility of the differentially private release. Specifically, we show that the amount of noise required to fulfill e-differential privacy can be reduced if noise is added to a k-anonymous version of the data set, where k-anonymity is reached through a specially designed microaggregation of all attributes. As a result of noise reduction, the analytical utility of the anonymized output data set is increased. The theoretical benefits of our proposal are illustrated in a practical setting with an empirical evaluation on a reference data set.