The Experts below are selected from a list of 3351 Experts worldwide ranked by ideXlab platform
Alan E Craddock - One of the best experts on this subject based on the ideXlab platform.
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the measurement of Privacy Preferences within marital relationships the relationship Privacy Preference scale
American Journal of Family Therapy, 1997Co-Authors: Alan E CraddockAbstract:Abstract This article proposes that the concept of Privacy Preference has relevance for marital therapy. Because of the specific nature and uniqueness of Privacy in intimate relationships, despite the existence of several general measures of Privacy Preferences, there is a need for a scale that is centered on aspects of Privacy Preferences of particular relevance to married or cohabiting couples. The article describes the Relational Privacy Preferences Scale (RPPS), which assesses individuals' Privacy Preferences in regard to Solitude, Reserve with Partner, Possessiveness, and Neighbor Avoidance, using items of specific relevance to marriage and cohabitation. The results of a field study assessing the RPPS show the scale to be reliable and to possess a sound factor structure. Data from a married or cohabiting subsample of the field sample reveal negative associations between Reserve with Partner and Solitude with several aspects of relationship satisfaction.
Byungho Park - One of the best experts on this subject based on the ideXlab platform.
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of promoting networking and protecting Privacy effects of defaults and regulatory focus on social media users Preference settings
Computers in Human Behavior, 2019Co-Authors: Hichang Cho, Sungjong Roh, Byungho ParkAbstract:Abstract Privacy research has debated whether Privacy decision-making is determined by users' stable Preferences (i.e., individual traits), Privacy calculus (i.e., cost-benefit analysis), or “responses on the spot” that vary across contexts. This study focuses on two factors—default setting as a contextual factor and regulatory focus as an individual difference factor—and examines the degree to which these factors affect social media users' decision-making when using Privacy Preference settings in a fictitious social networking site. The results, based on two experimental studies (study 1, n = 414; study 2, n = 213), show that default settings significantly affect users' Privacy Preferences, such that users choose the defaults or alternatives proximal to them. Study 2 shows that regulatory focus also affects Privacy decisions, such that users with a strong promotion focus select options favoring a higher social networking utility, perceiving lesser cognitive efforts and more confidence in decisions. Finally, we find a significant interaction effect between default setting and regulatory focus on perceived effort and confidence, suggesting that the default effect is contingent on users’ goal orientations (operationalized as regulatory focus). We discuss the implications for research and practice.
Hichang Cho - One of the best experts on this subject based on the ideXlab platform.
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of promoting networking and protecting Privacy effects of defaults and regulatory focus on social media users Preference settings
Computers in Human Behavior, 2019Co-Authors: Hichang Cho, Sungjong Roh, Byungho ParkAbstract:Abstract Privacy research has debated whether Privacy decision-making is determined by users' stable Preferences (i.e., individual traits), Privacy calculus (i.e., cost-benefit analysis), or “responses on the spot” that vary across contexts. This study focuses on two factors—default setting as a contextual factor and regulatory focus as an individual difference factor—and examines the degree to which these factors affect social media users' decision-making when using Privacy Preference settings in a fictitious social networking site. The results, based on two experimental studies (study 1, n = 414; study 2, n = 213), show that default settings significantly affect users' Privacy Preferences, such that users choose the defaults or alternatives proximal to them. Study 2 shows that regulatory focus also affects Privacy decisions, such that users with a strong promotion focus select options favoring a higher social networking utility, perceiving lesser cognitive efforts and more confidence in decisions. Finally, we find a significant interaction effect between default setting and regulatory focus on perceived effort and confidence, suggesting that the default effect is contingent on users’ goal orientations (operationalized as regulatory focus). We discuss the implications for research and practice.
Justin Zhan - One of the best experts on this subject based on the ideXlab platform.
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improved customers Privacy Preference policy
Granular Computing, 2007Co-Authors: Ran Wei, Justin ZhanAbstract:Companies tend to collect more and more data about their customers. This data is seen as useful for the company, but often customers do not wish to share it. Methods such as P3P for verifying a company's Privacy policy are available, however these do not provide much choice for the customer and they provide an all-or-nothing approach. In this paper, we improve the Privacy Preference policy approach to give customers more choices to make decisions on disclosing private information and make online companies more careful on their collection of customers' private information.
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a proposal towards customers Privacy Preference policy
International Conference on Machine Learning and Cybernetics, 2007Co-Authors: Ran Wei, Justin ZhanAbstract:Increasingly, companies hold more and more data about their customers. This data is seen as useful for the company, although often customers do not wish to share their personal data. Methods such as P3P for verifying a company's Privacy policy are available, however, they do not provide much choice to the customer, and they provide an all-or-nothing approach. We explore the Privacy Preference policy approach which attempts to align the Privacy Preferences of the individual with that of a company. We further critique this method and develop it to incorporate levels of importance and priority.
Yasushi Sakurai - One of the best experts on this subject based on the ideXlab platform.
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how well can a user s location Privacy Preferences be determined without using gps location data
IEEE Transactions on Emerging Topics in Computing, 2017Co-Authors: Takuya Maekawa, Naomi Yamashita, Yasushi SakuraiAbstract:The recent proliferation of GPS-enabled mobile phones has allowed people to share their current locations with others. Because disclosing one’s location can be valuable but risky, many services and studies employ a user’s GPS coordinates to determine automatically whether or not those coordinates can be disclosed by comparing them with handcrafted rules or Privacy models trained using the user’s actual Preferences. However, these approaches that employ GPS coordinates constitute a drain on a phone’s battery when the services assume continuous location sharing. In addition, recent positioning methods (assisted GPS and a WiFi-based positioning) rely on external location providers. That is, when a user’s current location Preference is determined using her coordinate point, her location information is disclosed to external providers even if this is not her wish. In this paper, we explore a way of learning a user’s location Privacy Preference using sensors that are energy saving and that do not rely on external providers. This enables us to save energy and protect a user’s Privacy when she is unwilling to disclose her location. Note that the machine learning based approach cannot deal well with a user’s private situations that are not included in its training data. So, this paper proposes a new model that can determine a user’s Privacy Preferences and handle such outlying situations.