Anonymity, Privacy, and Disclosure (APD) Triad on Social Networking Applications

While the average time people spend on their mobile apps continues to increase, the life cycle of using new social networking apps (SNA) remains relatively short, mostly due to privacy concern. For SNA users, it is important to know how the perception of anonymity and privacy concern determine the depth of disclosed information. For many SNA developers and practitioners, understanding the actual engagement of users on the platform is critical for measuring success of the app. Previous research has evaluated motivations/preventions of app usage and consequences of continuing usage. Despite efforts to understand the engagement with mobile devices and other users, there is little work in the Information Systems (IS) field to simultaneously investigate the triad of anonymity, privacy concern, and disclosure (APD) on continuous engagement with SNAs. Through the lens of contextual integrity of privacy, this research proposes a research model to investigate APD relationships with perceived and actual engagements with a new SNA. The research model is tested using a survey and actual usage data captured from users’ log files provided by mobile app developers. Results demonstrate how privacy is significantly related with actual engagement while anonymity relationship with actual engagement is fully mediated by perceived engagement

Current State of Personal Data Protection in Electronic Voting: Criteria and Indicator for Effective Implementation

The adoption of electronic voting has been done in various countries related to cost and time reduction operationally. On the other hand, recent publication has been informed several issues occurred such as technicality, reliability, security and privacy due to the compromised system were used. In small scale, there are certain group of people who want to exploit the vulnerabilities for their own benefit in the election, while in the greater scale, it can reduce public confidence to entrust the adoption of e-voting system to augment participation rate, to improve the quality of voting and to aid the political right effectively. This paper aims to investigate the characteristic of people demanding the legislative to address the criteria and indicator for effective implementation in electronic voting. By understanding the perception of voters in viewing current electoral regulation are essential to provide some ideas and opinions for better enhancement, either through recommendation and drafting related legislation to cater the needs

How to Increase Smart Home Security and Privacy Risk Perception

With continuous technological advancements, our homes become smarter by interconnecting more and more devices. Smart homes provide many advantages. However, they also introduce new privacy and security risks. Recent studies show that only a few people are aware of abstract risks, and most people are not aware of specific negative consequences. We developed a privacy and security awareness intervention for people who want to inform themselves about risks in the smart home context. Our intervention is based on research literature on risk perception and feedback from both lay users and security and privacy experts. We evaluated our intervention regarding its influence on participants’ perceived threat, privacy attitude, motivation to avoid threats, willingness to pay, and time commitment to configure protective measures. The results of this evaluation show a significant increase for all these aspects. We also compared our intervention to information that users could obtain during an Internet search on the topic. In this comparison, our intervention evokes a significantly higher perceived threat and privacy attitude. It showed no significant difference for the other three scales. We discuss our findings in light of related work

BFF: A tool for eliciting tie strength and user communities in social networking services

The final publication is available at Springer via http://dx.doi.org/ 10.1007/s10796-013-9453-6The use of social networking services (SNSs) such as Facebook has explosively grown in the last few years. Users see these SNSs as useful tools to find friends and interact with them. Moreover, SNSs allow their users to share photos, videos, and express their thoughts and feelings. However, users are usually concerned about their privacy when using SNSs. This is because the public image of a subject can be affected by photos or comments posted on a social network. In this way, recent studies demonstrate that users are demanding better mechanisms to protect their privacy. An appropriate approximation to solve this could be a privacy assistant software agent that automatically suggests a privacy policy for any item to be shared on a SNS. The first step for developing such an agent is to be able to elicit meaningful information that can lead to accurate privacy policy predictions. In particular, the information needed is user communities and the strength of users' relationships, which, as suggested by recent empirical evidence, are the most important factors that drive disclosure in SNSs. Given the number of friends that users can have and the number of communities they may be involved on, it is infeasible that users are able to provide this information without the whole eliciting process becoming confusing and time consuming. In this work, we present a tool called Best Friend Forever (BFF) that automatically classifies the friends of a user in communities and assigns a value to the strength of the relationship ties to each one. We also present an experimental evaluation involving 38 subjects that showed that BFF can significantly alleviate the burden of eliciting communities and relationship strength.This work has been partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, and TIN 2008-04446 and PROMETEO II/2013/019 projects. 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BFF: A Tool for Eliciting Tie Strength and User Communities in Social Networking Services

López Fogués, R.; Such Aparicio, JM.; Espinosa Minguet, AR.; García-Fornes, A. (2014 Abstract The use of social networking services (SNSs) such as Facebook has explosively grown in the last few years. Users see these SNSs as useful tools to find friends and interact with them. Moreover, SNSs allow their users to share photos, videos, and express their thoughts and feelings. However, users are usually concerned about their privacy when using SNSs. This is because the public image of a subject can be affected by photos or comments posted on a social network. In this way, recent studies demonstrate that users are demanding better mechanisms to protect their privacy. An appropriate approximation to solve this could be a privacy assistant software agent that automatically suggests a privacy policy for any item to be shared on a SNS. The first step for developing such an agent is to be able to elicit meaningful information that can lead to accurate privacy policy predictions. In particular, the information needed is user communities and the strength of users' relationships, which, as suggested by recent empirical evidence, are the most important factors that drive disclosure in SNSs. Given the number of friends that users can have and the number of communities they may be involved on, it is infeasible that users are able to provide this information without the whole eliciting process becoming confusing and time consuming. In this work, we present a tool called Best Friend Forever (BFF) that automatically classifies the friends of a user in communities and assigns a value to the strength of the relationship ties to each one. We also present an experimental evaluation involving 38 subjects that showed that BFF can significantly alleviate the burden of eliciting communities and relationship strength

Investigating People’s Privacy Risk Perception

Although media reports often warn about risks associated with using privacy-threatening technologies , most lay users lack awareness of particular adverse consequences that could result from this usage. Since this might lead them to underestimate the risks of data collection, we investigate how lay users perceive different abstract and specific privacy risks. To this end, we conducted a survey with 942 participants in which we asked them to rate nine different privacy risk scenarios in terms of probability and severity. The survey included abstract risk scenarios as well as specific risk scenarios, which describe specifically how collected data can be abused, e.g., to stalk someone or to plan burglaries. To gain broad insights into people\u27s risk perception, we considered three use cases: Online Social Networks (OSN), smart home, and smart health devices. Our results suggest that abstract and specific risk scenarios are perceived differently, with abstract risk scenarios being evaluated as likely, but only moderately severe, whereas specific risk scenarios are considered to be rather severe, but only moderately likely. People, thus, do not seem to be aware of specific privacy risks when confronted with an abstract risk scenario. Hence, privacy researchers or activists should make people aware of what collected and analyzed data can be used for when abused (by the service or even an unauthorized third party)

Privacy in the Sharing Economy

Contemporary C2C platforms, such as Airbnb, have exhibited considerable growth in recent years and are projected to continue doing so in the future. These novel consumer-to-consumer marketplaces have started to obliterate the boundaries between private and economic spheres. Marketing personal resources online is inherently associated with the disclosure of personal and sometimes intimate information. This raises unprecedented questions of privacy. Yet, there is so far little research on the role of privacy considerations in the sharing economy literature. Leveraging the theoretical perspective of privacy calculus, we address this gap by investigating how privacy concerns and economic prospects shape a potential provider’s intentions to share via different communication channels. We relate privacy concerns back to the provider’s perceptions of the audience. We evaluate our research model by means of a scenario-based online survey, providing broad support for our reasoning