Balancing privacy concerns in the adoption of Location-Based Services: an empirical analysis

2011 ◽  
Vol 9 (1/2) ◽  
pp. 118 ◽  
Author(s):  
Sumeet Gupta ◽  
Heng Xu ◽  
Xialong Zhang
Keyword(s):  
Empirical Analysis ◽  
Location Based Services ◽  
Privacy Concerns

scholarly journals Privacy vs. Reward in Indoor Location-Based Services

2016 ◽  
Vol 2016 (4) ◽  
pp. 102-122 ◽  
Author(s):  
Kassem Fawaz ◽  
Kyu-Han Kim ◽  
Kang G. Shin
Keyword(s):  
Location Privacy ◽  
Indoor Localization ◽  
Service Providers ◽  
Location Based Services ◽  
Location Information ◽  
Privacy Concerns ◽  
Indoor Location ◽  
Location Data ◽  
Personalized Services ◽  
Privacy Risks

AbstractWith the advance of indoor localization technology, indoor location-based services (ILBS) are gaining popularity. They, however, accompany privacy concerns. ILBS providers track the users’ mobility to learn more about their behavior, and then provide them with improved and personalized services. Our survey of 200 individuals highlighted their concerns about this tracking for potential leakage of their personal/private traits, but also showed their willingness to accept reduced tracking for improved service. In this paper, we propose PR-LBS (Privacy vs. Reward for Location-Based Service), a system that addresses these seemingly conflicting requirements by balancing the users’ privacy concerns and the benefits of sharing location information in indoor location tracking environments. PR-LBS relies on a novel location-privacy criterion to quantify the privacy risks pertaining to sharing indoor location information. It also employs a repeated play model to ensure that the received service is proportionate to the privacy risk. We implement and evaluate PR-LBS extensively with various real-world user mobility traces. Results show that PR-LBS has low overhead, protects the users’ privacy, and makes a good tradeoff between the quality of service for the users and the utility of shared location data for service providers.

scholarly journals Privacy preservation in mobile social networks

2019 ◽  
Author(s):  
◽  
Douglas Steiert
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Networking ◽  
Privacy Preservation ◽  
Location Based Services ◽  
Mobile Social Networks ◽  
Privacy Concerns ◽  
Average Person ◽  
Effectiveness And Efficiency ◽  
Privacy Breaches

In this day and age with the prevalence of smartphones, networking has evolved in an intricate and complex way. With the help of a technology-driven society, the term "social networking" was created and came to mean using media platforms such as Myspace, Facebook, and Twitter to connect and interact with friends, family, or even complete strangers. Websites are created and put online each day, with many of them possessing hidden threats that the average person does not think about. A key feature that was created for vast amount of utility was the use of location-based services, where many websites inform their users that the website will be using the users' locations to enhance the functionality. However, still far too many websites do not inform their users that they may be tracked, or to what degree. In a similar juxtaposed scenario, the evolution of these social networks has allowed countless people to share photos with others online. While this seems harmless at face-value, there may be times in which people share photos of friends or other non-consenting individuals who do not want that picture viewable to anyone at the photo owner's control. There exists a lack of privacy controls for users to precisely de fine how they wish websites to use their location information, and for how others may share images of them online. This dissertation introduces two models that help mitigate these privacy concerns for social network users. MoveWithMe is an Android and iOS application which creates decoys that move locations along with the user in a consistent and semantically secure way. REMIND is the second model that performs rich probability calculations to determine which friends in a social network may pose a risk for privacy breaches when sharing images. Both models have undergone extensive testing to demonstrate their effectiveness and efficiency.

scholarly journals Achieving the Optimal k-Anonymity for Content Privacy in Interactive Cyberphysical Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Jinbao Wang ◽  
Ling Tian ◽  
Yan Huang ◽  
Donghua Yang ◽  
Hong Gao
Keyword(s):  
Health Care ◽  
Differential Privacy ◽  
Daily Life ◽  
Real Life ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Cyberphysical Systems ◽  
Privacy Concerns ◽  
Automatic Driving ◽  
Made In

Modern applications and services leveraged by interactive cyberphysical systems (CPS) are providing significant convenience to our daily life in various aspects at present. Clients submit their requests including query contents to CPS servers to enjoy diverse services such as health care, automatic driving, and location-based services. However, privacy concerns arise at the same time. Content privacy is recognized and a lot of efforts have been made in the literature of privacy preserving in interactive cyberphysical systems such as location-based services. Nevertheless, neither the cloaking based solutions nor existing client based solutions have achieved effective content privacy by optimizing proper content privacy metrics. In this paper we formulate the problem of achieving the optimal content privacy in interactive cyberphysical systems using k-anonymity solutions based on two content privacy metrics, which are defined using the concepts of entropy and differential privacy. Then we propose an algorithm, Multilayer Alignment (MLA), to establish k-anonymity mechanisms for preserving content privacy in interactive cyberphysical systems. Our proposed MLA is theoretically proved to achieve the optimal content privacy in terms of both the entropy based and the differential privacy mannered content privacy metrics. Evaluation based on real-life datasets is conducted, and the evaluation results validate the effectiveness of our proposed algorithm.

Factors affecting customers’ intention to use of location-based services (LBS) in the lodging industry

2017 ◽  
Vol 8 (3) ◽  
pp. 337-356 ◽  
Author(s):  
Jungsun (Sunny) Kim ◽  
Sungsik Yoon ◽  
Dina Marie V. Zemke
Keyword(s):  
Structural Equation ◽  
Online Survey ◽  
Location Based Services ◽  
Equation Modeling ◽  
Content Type ◽  
Factors Affecting ◽  
Privacy Concerns ◽  
Four Dimensions ◽  
Secondary Use ◽  
Hotel Customers

Purpose The purpose of this study is to investigate the determinants of customers’ intentions to use location-based services (LBS) offered by a hotel. The study examined whether hotel customers’ coupon proneness, trust, privacy concerns and familiarity with LBS are significant determinants of their intentions to use LBS. Design/methodology/approach An online survey using a scenario-based narrative was administered to collect data from participants who have smartphones and have stayed at a full-service hotel within the previous 12 months. A research model tested data collected from 402 hotel customers, using confirmatory factor analysis and structural equation modeling. Findings Three proposed determinants (i.e. familiarity, coupon proneness and trust) positively influenced customers’ intentions to use LBS. Out of the four dimensions of privacy concerns (concerns of collection, error, unauthorized secondary use and improper access), only concerns about data collection negatively influenced customers’ intentions to use a hotel’s LBS. Originality/value This study extends the literature on LBS adoption and other technology with privacy issues by modifying existing models and empirically testing it in the new context of hotels.

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