5,858 research outputs found

    "If You Can't Beat them, Join them": A Usability Approach to Interdependent Privacy in Cloud Apps

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    Cloud storage services, like Dropbox and Google Drive, have growing ecosystems of 3rd party apps that are designed to work with users' cloud files. Such apps often request full access to users' files, including files shared with collaborators. Hence, whenever a user grants access to a new vendor, she is inflicting a privacy loss on herself and on her collaborators too. Based on analyzing a real dataset of 183 Google Drive users and 131 third party apps, we discover that collaborators inflict a privacy loss which is at least 39% higher than what users themselves cause. We take a step toward minimizing this loss by introducing the concept of History-based decisions. Simply put, users are informed at decision time about the vendors which have been previously granted access to their data. Thus, they can reduce their privacy loss by not installing apps from new vendors whenever possible. Next, we realize this concept by introducing a new privacy indicator, which can be integrated within the cloud apps' authorization interface. Via a web experiment with 141 participants recruited from CrowdFlower, we show that our privacy indicator can significantly increase the user's likelihood of choosing the app that minimizes her privacy loss. Finally, we explore the network effect of History-based decisions via a simulation on top of large collaboration networks. We demonstrate that adopting such a decision-making process is capable of reducing the growth of users' privacy loss by 70% in a Google Drive-based network and by 40% in an author collaboration network. This is despite the fact that we neither assume that users cooperate nor that they exhibit altruistic behavior. To our knowledge, our work is the first to provide quantifiable evidence of the privacy risk that collaborators pose in cloud apps. We are also the first to mitigate this problem via a usable privacy approach.Comment: Authors' extended version of the paper published at CODASPY 201

    Interdependent Privacy

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    Your Privacy Is Your Friend's Privacy: Examining Interdependent Information Disclosure on Online Social Networks

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    The highly interactive nature of interpersonal communication on online social networks (OSNs) impels us to think about privacy as a communal matter, with users' private information being revealed by not only their own voluntary disclosures, but also the activities of their social ties. The current privacy literature has identified two types of information disclosures in OSNs: self-disclosure, i.e., the disclosure of an OSN user's private information by him/herself; and co-disclosure, i.e., the disclosure of the user's private information by other users. Although co-disclosure has been increasingly identified as a new source of privacy threat inherent to the OSN context, few systematic attempts have been made to provide a framework for understanding the commonalities and distinctions between self- vs. co-disclosure, especially pertaining to different types of private information. To address this gap, this paper presents a data-driven study that builds upon an innovative measurement for quantifying the extent to which others' co-disclosure could lead to actual privacy harm. The results demonstrate the significant harm caused by co-disclosure and illustrate the differences between the identity elements revealed through self- and co-disclosure

    Exploring Information Disclosure in Location-based Services: U.S. vs. German Populations

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    Location-based services (LBSs) have enabled users to obtain context-specific and personalized services owing to advances in mobile technologies and location analytics. Since location data are classified as personally identifiable, the sharing of locations on LBSs has privacy implications. We employ a privacy calculus lens to study users’ attitudes toward location-information sharing. We explore the role of cultural and institutional environments in users’ disclosure behaviors in two populations: the U.S. and Germany. Our results show similarities between the two samples, despite differences in cultural backgrounds and regulations. Contextualization is a highly valued benefit for LBS users, while monetary rewards are not yet foreseen as potential benefits. Location-information disclosure is not uniform; it varies depending on the sharing parties and the information extent or sensitivity. LBS users have high privacy risk perceptions and low trust in service providers and government regulations to protect their privacy and location-information from misuse

    Your Privacy Is Your Friend\u27s Privacy: Examining Interdependent Information Disclosure on Online Social Networks

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    The highly interactive nature of interpersonal communication on online social networks (OSNs) impels us to think about privacy as a communal matter, with users\u27 private information being revealed by not only their own voluntary disclosures, but also the activities of their social ties. The current privacy literature has identified two types of information disclosures in OSNs: self-disclosure, i.e., the disclosure of an OSN user\u27s private information by him/herself; and co-disclosure, i.e., the disclosure of the user\u27s private information by other users. Although co-disclosure has been increasingly identified as a new source of privacy threat inherent to the OSN context, few systematic attempts have been made to provide a framework for understanding the commonalities and distinctions between self- vs. co-disclosure, especially pertaining to different types of private information. To address this gap, this paper presents a data-driven study that builds upon an innovative measurement for quantifying the extent to which others\u27 co-disclosure could lead to actual privacy harm. The results demonstrate the significant harm caused by co-disclosure and illustrate the differences between the identity elements revealed through self- and co-disclosure

    Critical Management Issues for Implementing RFID in Supply Chain Management

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    The benefits of radio frequency identification (RFID) technology in the supply chain are fairly compelling. It has the potential to revolutionise the efficiency, accuracy and security of the supply chain with significant impact on overall profitability. A number of companies are actively involved in testing and adopting this technology. It is estimated that the market for RFID products and services will increase significantly in the next few years. Despite this trend, there are major impediments to RFID adoption in supply chain. While RFID systems have been around for several decades, the technology for supply chain management is still emerging. We describe many of the challenges, setbacks and barriers facing RFID implementations in supply chains, discuss the critical issues for management and offer some suggestions. In the process, we take an in-depth look at cost, technology, standards, privacy and security and business process reengineering related issues surrounding RFID technology in supply chains

    UNDERSTANDING USER PERCEPTIONS AND PREFERENCES FOR MASS-MARKET INFORMATION SYSTEMS – LEVERAGING MARKET RESEARCH TECHNIQUES AND EXAMPLES IN PRIVACY-AWARE DESIGN

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    With cloud and mobile computing, a new category of software products emerges as mass-market information systems (IS) that addresses distributed and heterogeneous end-users. Understanding user requirements and the factors that drive user adoption are crucial for successful design of such systems. IS research has suggested several theories and models to explain user adoption and intentions to use, among them the IS Success Model and the Technology Acceptance Model (TAM). Although these approaches contribute to theoretical understanding of the adoption and use of IS in mass-markets, they are criticized for not being able to drive actionable insights on IS design as they consider the IT artifact as a black-box (i.e., they do not sufficiently address the system internal characteristics). We argue that IS needs to embrace market research techniques to understand and empirically assess user preferences and perceptions in order to integrate the "voice of the customer" in a mass-market scenario. More specifically, conjoint analysis (CA), from market research, can add user preference measurements for designing high-utility IS. CA has gained popularity in IS research, however little guidance is provided for its application in the domain. We aim at supporting the design of mass-market IS by establishing a reliable understanding of consumer’s preferences for multiple factors combing functional, non-functional and economic aspects. The results include a “Framework for Conjoint Analysis Studies in IS” and methodological guidance for applying CA. We apply our findings to the privacy-aware design of mass-market IS and evaluate their implications on user adoption. We contribute to both academia and practice. For academia, we contribute to a more nuanced conceptualization of the IT artifact (i.e., system) through a feature-oriented lens and a preference-based approach. We provide methodological guidelines that support researchers in studying user perceptions and preferences for design variations and extending that to adoption. Moreover, the empirical studies for privacy- aware design contribute to a better understanding of the domain specific applications of CA for IS design and evaluation with a nuanced assessment of user preferences for privacy-preserving features. For practice, we propose guidelines for integrating the voice of the customer for successful IS design. -- Les technologies cloud et mobiles ont fait Ă©merger une nouvelle catĂ©gorie de produits informatiques qui s’adressent Ă  des utilisateurs hĂ©tĂ©rogĂšnes par le biais de systĂšmes d'information (SI) distribuĂ©s. Les termes “SI de masse” sont employĂ©s pour dĂ©signer ces nouveaux systĂšmes. Une conception rĂ©ussie de ceux-ci passe par une phase essentielle de comprĂ©hension des besoins et des facteurs d'adoption des utilisateurs. Pour ce faire, la recherche en SI suggĂšre plusieurs thĂ©ories et modĂšles tels que le “IS Success Model” et le “Technology Acceptance Model”. Bien que ces approches contribuent Ă  la comprĂ©hension thĂ©orique de l'adoption et de l'utilisation des SI de masse, elles sont critiquĂ©es pour ne pas ĂȘtre en mesure de fournir des informations exploitables sur la conception de SI car elles considĂšrent l'artefact informatique comme une boĂźte noire. En d’autres termes, ces approches ne traitent pas suffisamment des caractĂ©ristiques internes du systĂšme. Nous soutenons que la recherche en SI doit adopter des techniques d'Ă©tude de marchĂ© afin de mieux intĂ©grer les exigences du client (“Voice of Customer”) dans un scĂ©nario de marchĂ© de masse. Plus prĂ©cisĂ©ment, l'analyse conjointe (AC), issue de la recherche sur les consommateurs, peut contribuer au dĂ©veloppement de systĂšme SI Ă  forte valeur d'usage. Si l’AC a gagnĂ© en popularitĂ© au sein de la recherche en SI, des recommandations quant Ă  son utilisation dans ce domaine restent rares. Nous entendons soutenir la conception de SI de masse en facilitant une identification fiable des prĂ©fĂ©rences des consommateurs sur de multiples facteurs combinant des aspects fonctionnels, non-fonctionnels et Ă©conomiques. Les rĂ©sultats comprennent un “Cadre de rĂ©fĂ©rence pour les Ă©tudes d'analyse conjointe en SI” et des recommandations mĂ©thodologiques pour l'application de l’AC. Nous avons utilisĂ© ces contributions pour concevoir un SI de masse particuliĂšrement sensible au respect de la vie privĂ©e des utilisateurs et nous avons Ă©valuĂ© l’impact de nos recherches sur l'adoption de ce systĂšme par ses utilisateurs. Ainsi, notre travail contribue tant Ă  la thĂ©orie qu’à la pratique des SI. Pour le monde universitaire, nous contribuons en proposant une conceptualisation plus nuancĂ©e de l'artefact informatique (c'est-Ă -dire du systĂšme) Ă  travers le prisme des fonctionnalitĂ©s et par une approche basĂ©e sur les prĂ©fĂ©rences utilisateurs. Par ailleurs, les chercheurs peuvent Ă©galement s'appuyer sur nos directives mĂ©thodologiques pour Ă©tudier les perceptions et les prĂ©fĂ©rences des utilisateurs pour diffĂ©rentes variations de conception et Ă©tendre cela Ă  l'adoption. De plus, nos Ă©tudes empiriques sur la conception d’un SI de masse sensible au respect de la vie privĂ©e des utilisateurs contribuent Ă  une meilleure comprĂ©hension de l’application des techniques CA dans ce domaine spĂ©cifique. Nos Ă©tudes incluent notamment une Ă©valuation nuancĂ©e des prĂ©fĂ©rences des utilisateurs sur des fonctionnalitĂ©s de protection de la vie privĂ©e. Pour les praticiens, nous proposons des lignes directrices qui permettent d’intĂ©grer les exigences des clients afin de concevoir un SI rĂ©ussi

    A Game Theoretic Approach to Balance Privacy Risks and Familial Benefits

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    As recreational genomics continues to grow in its popularity, many people are afforded the opportunity to share their genomes in exchange for various services, including third-party interpretation (TPI) tools, to understand their predisposition to health problems and, based on genome similarity, to find extended family members. At the same time, these services have increasingly been reused by law enforcement to track down potential criminals through family members who disclose their genomic information. While it has been observed that many potential users shy away from such data sharing when they learn that their privacy cannot be assured, it remains unclear how potential users’ valuations of the service will affect a population’s behavior. In this paper, we present a game theoretic framework to model interdependent privacy challenges in genomic data sharing online. Through simulations, we find that in addition to the boundary cases when (1) no player and (2) every player joins, there exist pure-strategy Nash equilibria when a relatively small portion of players choose to join the genomic database. The result is consistent under different parametric settings. We further examine the stability of Nash equilibria and illustrate that the only equilibrium that is resistant to a random dropping of players is when all players join the genomic database. Finally, we show that when players consider the impact that their data sharing may have on their relatives, the only pure strategy Nash equilibria are when either no player or every player shares their genomic data
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