Consumer's Privacy Concerns and Willingness to Provide Personal Information in Location-Based Services

Author(s):  
Sae Sol Choi ◽  
Mun-Kee Choi
2016 ◽  
Vol 2016 (4) ◽  
pp. 102-122 ◽  
Author(s):  
Kassem Fawaz ◽  
Kyu-Han Kim ◽  
Kang G. Shin

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.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jillian Carmody ◽  
Samir Shringarpure ◽  
Gerhard Van de Venter

Purpose The purpose of this paper is to demonstrate privacy concerns arising from the rapidly increasing advancements and use of artificial intelligence (AI) technology and the challenges of existing privacy regimes to ensure the on-going protection of an individual’s sensitive private information. The authors illustrate this through a case study of energy smart meters and suggest a novel combination of four solutions to strengthen privacy protection. Design/methodology/approach The authors illustrate how, through smart meter obtained energy data, home energy providers can use AI to reveal private consumer information such as households’ electrical appliances, their time and frequency of usage, including number and model of appliance. The authors show how this data can further be combined with other data to infer sensitive personal information such as lifestyle and household income due to advances in AI technologies. Findings The authors highlight data protection and privacy concerns which are not immediately obvious to consumers due to the capabilities of advanced AI technology and its ability to extract sensitive personal information when applied to large overlapping granular data sets. Social implications The authors question the adequacy of existing privacy legislation to protect sensitive inferred consumer data from AI-driven technology. To address this, the authors suggest alternative solutions. Originality/value The original value of this paper is that it illustrates new privacy issues brought about by advances in AI, failings in current privacy legislation and implementation and opens the dialog between stakeholders to protect vulnerable consumers.


Author(s):  
Ajaysinh Devendrasinh Rathod ◽  
Saurabh Shah ◽  
Vivaksha J. Jariwala

In recent trends, growth of location based services have been increased due to the large usage of cell phones, personal digital assistant and other devices like location based navigation, emergency services, location based social networking, location based advertisement, etc. Users are provided with important information based on location to the service provider that results the compromise with their personal information like user’s identity, location privacy etc. To achieve location privacy of the user, cryptographic technique is one of the best technique which gives assurance. Location based services are classified as Trusted Third Party (TTP) & without Trusted Third Party that uses cryptographic approaches. TTP free is one of the prominent approach in which it uses peer-to-peer model. In this approach, important users mutually connect with each other to form a network to work without the use of any person/server. There are many existing approaches in literature for privacy preserving location based services, but their solutions are at high cost or not supporting scalability.  In this paper, our aim is to propose an approach along with algorithms that will help the location based services (LBS) users to provide location privacy with minimum cost and improve scalability.


Author(s):  
Eko Wahyu Tyas Darmaningrat ◽  
Hanim Maria Astuti ◽  
Fadhila Alfi

Background: Teenagers in Indonesia have an open nature and satisfy their desire to exist by uploading photos or videos and writing posts on Instagram. The habit of uploading photos, videos, or writings containing their personal information can be dangerous and potentially cause user privacy problems. Several criminal cases caused by information misuse have occurred in Indonesia.Objective: This paper investigates information privacy concerns among Instagram users in Indonesia, more specifically amongst college students, the largest user group of Instagram in Indonesia.Methods: This study referred to the Internet Users' Information Privacy Concerns (IUIPC) method by collecting data through the distribution of online questionnaires and analyzed the data by using Structural Equation Modelling (SEM).Results: The research finding showed that even though students are mindful of the potential danger of information misuse in Instagram, it does not affect their intention to use Instagram. Other factors that influence Indonesian college students' trust are Instagram's reputation, the number of users who use Instagram, the ease of using Instagram, the skills and knowledge of Indonesian students about Instagram, and the privacy settings that Instagram has.Conclusion: The awareness and concern of Indonesian college students for information privacy will significantly influence the increased risk awareness of information privacy. However, the increase in risk awareness does not directly affect Indonesian college students' behavior to post their private information on Instagram.


2021 ◽  
Author(s):  
Julie Gustavel

Issues about informational privacy have emerged in tandem with the escalating increase in nformation stored in electronic formats. Data protection is a pressing issue not only because files of personal information are being kept in greater detail and for longer periods of time, but also because the data can be retrieved and compared or matched without delay, regardless of geography. While defenders of information technology cite efficiency and safety among the countervailing benefits, concerns from an increasingly tech-savvy public have introduced a sense of urgency to demand tough legislation. Although many studies have provided evidence of online privacy concerns, few have explored the nature of the concern in detail, especially in terms of government policy for our new online environment. Bill C-6, Canada's recent legislative action, has provided a practical basis from which to appraise governments' role in privacy protection. With this in mind, the paper will be divided into two parts. Part one will be undertaken to: (A) evaluate the arguments of critics as well as defenders of contemporary record-keeping practices and the philosophical conceptions of privacy, which underlie them; and, using these themes (B) provide a comprehensive assessment of the effectiveness of Bill C- 6, examining the ways in which policy makers have begun to treat privacy as both a commodity and a secondary adjunct to business activity. Part two of the paper, purposes a series of recommendations or, more specifically, a framework for Bill C-6 that would, more effectively, protect individual privacy from private entities, who collect online data.


2019 ◽  
Author(s):  
◽  
Youssef Ramzi Mansour

Big data is a relatively new concept that refers to the enormous amount of data generated in a new era where people are selling, buying, paying dues, managing their health and communicating over the internet. It becomes natural that generated data will be analyzed for the purposes of smart advertising and social statistical studies. Social data analytics is the concept of micro-studying users interactions through data obtained often from social networking services, the concept also known as “social mining” offers tremendous opportunities to support decision making through recommendation systems widely used by e-commerce mainly. With these new opportunities comes the problematic of social media users privacy concerns as protecting personal information over the internet has become a controversial issue among social network providers and users. In this study we identify and describe various privacy concerns and related platforms as well as the legal frameworks governing the protection of personal information in different jurisdictions. Furthermore we discuss the Facebook and Cambridge Analytica Ltd incident as an example.


Author(s):  
Anna Rohunen ◽  
Jouni Markkula

Personal data is increasingly collected with the support of rapidly advancing information and communication technology, which raises privacy concerns among data subjects. In order to address these concerns and offer the full benefits of personal data intensive services to the public, service providers need to understand how to evaluate privacy concerns in evolving service contexts. By analyzing the earlier used privacy concerns evaluation instruments, we can learn how to adapt them to new contexts. In this article, the historical development of the most widely used privacy concerns evaluation instruments is presented and analyzed regarding privacy concerns' dimensions. Privacy concerns' core dimensions, and the types of context dependent dimensions, to be incorporated into evaluation instruments are identified. Following this, recommendations on how to utilize the existing evaluation instruments are given, as well as suggestions for future research dealing with validation and standardization of the instruments.


Author(s):  
Monica Grosso ◽  
Sandro Castaldo

Today companies are more and more interested in collecting personal information from customers in order to deliver goods and services effectively and to improve their Marketing database and CRM efficacy. However, the ease with which data can be acquired and disseminated, also thanks to the digital technologies, has led to many potential customers demonstrating growing concerns and ethical issues about disclosing personal information. On this topic it is difficult to make too many generalizations, since the cultural differences and the different country regulations seem to weigh significantly.


Author(s):  
Joseph Kwame Adjei

Monetization of personal identity information has become a major component of modern business models, contributing to dramatic innovations in the collection, aggregation, and use of personal information. This phenomenon is commonplace given that parties to business transactions and social interactions usually rely on the issue of claims and disclosure of unique attributes and credentials for proof and verification of identity. However, the heightened societal information privacy concerns and the diminishing level of trust between transacting parties make such attempts to monetize personal information a very risky endeavor. This chapter examines the major technological and regulatory imperatives in the monetization of personal identity information. The resulting monetization model provides an important source of reference for effective monetization of personal information.


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