scholarly journals Privacy-preserving recommendations in context-aware mobile environments

2017 ◽  
Vol 25 (1) ◽  
pp. 62-79 ◽  
Author(s):  
Nikolaos Polatidis ◽  
Christos K. Georgiadis ◽  
Elias Pimenidis ◽  
Emmanouil Stiakakis
Keyword(s):  
Recommender Systems ◽  
Privacy Protection ◽  
Personal Information ◽  
Contextual Information ◽  
Context Aware ◽  
User Privacy ◽  
Data Set ◽  
Content Type ◽  
Privacy Concerns ◽  
Usability Tests

Purpose This paper aims to address privacy concerns that arise from the use of mobile recommender systems when processing contextual information relating to the user. Mobile recommender systems aim to solve the information overload problem by recommending products or services to users of Web services on mobile devices, such as smartphones or tablets, at any given point in time and in any possible location. They use recommendation methods, such as collaborative filtering or content-based filtering and use a considerable amount of contextual information to provide relevant recommendations. However, because of privacy concerns, users are not willing to provide the required personal information that would allow their views to be recorded and make these systems usable. Design/methodology/approach This work is focused on user privacy by providing a method for context privacy-preservation and privacy protection at user interface level. Thus, a set of algorithms that are part of the method has been designed with privacy protection in mind, which is done by using realistic dummy parameter creation. To demonstrate the applicability of the method, a relevant context-aware data set has been used to run performance and usability tests. Findings The proposed method has been experimentally evaluated using performance and usability evaluation tests and is shown that with a small decrease in terms of performance, user privacy can be protected. Originality/value This is a novel research paper that proposed a method for protecting the privacy of mobile recommender systems users when context parameters are used.

scholarly journals Mobile Context-Aware Systems: Technologies, Resources and Applications

2016 ◽  
Vol 10 (2) ◽  
pp. 25 ◽  
Author(s):  
Alejandro Rivero-Rodriguez ◽  
Paolo Pileggi ◽  
Ossi Antero Nykänen
Keyword(s):  
Personal Information ◽  
Contextual Information ◽  
General Information ◽  
Context Aware ◽  
User Privacy ◽  
Contextual Data ◽  
Potential Applications ◽  
Mobile Context ◽  
Available Information ◽  
Further Development

Mobile applications often adapt their behavior according to user context, however, they are often limited to consider few sources of contextual information, such as user position or language. This article reviews existing work in context-aware systems (CAS), e.g., how to model context, and discusses further development of CAS and its potential applications by looking at available information, methods and technologies. Social Media seems to be an interesting source of personal information when appropriately exploited. In addition, there are many types of general information, ranging from weather and public transport to information of books and museums. These information sources can be combined in previously unexplored ways, enabling the development of smarter mobile services in different domains. Users are, however, reluctant to provide their personal information to applications; therefore, there is a crave for new regulations and systems that allow applications to use such contextual data without compromising the user privacy.

Dynamic Control Mechanisms for User Privacy Enhancement

Cyber Crime ◽  
2013 ◽  
pp. 534-556
Author(s):  
Amr Ali Eldin
Keyword(s):  
Decision Making ◽  
Private Information ◽  
Contextual Information ◽  
Dynamic Control ◽  
Control Mechanisms ◽  
Context Aware ◽  
User Privacy ◽  
Privacy Concerns ◽  
Fuzzy Logic Approach ◽  
Expected Benefits

Despite the expected benefits behind context-awareness and the need for developing more and more context-aware applications, we enunciate that privacy represents a major challenge for the success and widespread adoption of these services. This is due to the collection of huge amount of users’ contextual information, which would highly threaten their privacy concerns. Controlling users’ information collection represents a logical way to let users get more acquainted with these context-aware services. Additionally, this control requires users to be able to make consent decisions which face a high degree of uncertainty due to the nature of this environment and the lack of experience from the user side with information collectors’ privacy policies. Therefore, intelligent techniques are required in order to deal with this uncertainty. In this chapter, the auhtors propose a consent decision-making mechanism, ShEM, which allows users to exert automatic and manual control over their private information. An enhanced fuzzy logic approach was developed for the automatic decision making process. The proposed mechanism has been prototyped and integrated in a UMTS location-based services testbed on a university campus. Users have experienced the services in real time. A survey of users’ responses on the privacy functionality has been carried out and analyzed as well. Users’ response on the privacy functionality was positive. Additionally, results obtained showed that a combination of both manual and automatic privacy control modes in one approach is more likely to be accepted than only a complete automatic or a complete manual privacy control.

Dynamic Control Mechanisms for User Privacy Enhancement

2011 ◽  
pp. 115-137
Author(s):  
Amr Ali Eldin
Keyword(s):  
Decision Making ◽  
Private Information ◽  
Contextual Information ◽  
Dynamic Control ◽  
Control Mechanisms ◽  
Context Aware ◽  
User Privacy ◽  
Privacy Concerns ◽  
Fuzzy Logic Approach ◽  
Expected Benefits

Despite the expected benefits behind context-awareness and the need for developing more and more context-aware applications, we enunciate that privacy represents a major challenge for the success and widespread adoption of these services. This is due to the collection of huge amount of users’ contextual information, which would highly threaten their privacy concerns. Controlling users’ information collection represents a logical way to let users get more acquainted with these context-aware services. Additionally, this control requires users to be able to make consent decisions which face a high degree of uncertainty due to the nature of this environment and the lack of experience from the user side with information collectors’ privacy policies. Therefore, intelligent techniques are required in order to deal with this uncertainty. In this chapter, the auhtors propose a consent decision-making mechanism, ShEM, which allows users to exert automatic and manual control over their private information. An enhanced fuzzy logic approach was developed for the automatic decision making process. The proposed mechanism has been prototyped and integrated in a UMTS location-based services testbed on a university campus. Users have experienced the services in real time. A survey of users’ responses on the privacy functionality has been carried out and analyzed as well. Users’ response on the privacy functionality was positive. Additionally, results obtained showed that a combination of both manual and automatic privacy control modes in one approach is more likely to be accepted than only a complete automatic or a complete manual privacy control.

scholarly journals Differential Privacy and Bayesian for Context-Aware Recommender Systems

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Bayesian Network ◽  
Recommender Systems ◽  
Privacy Protection ◽  
Recommendation System ◽  
Differential Privacy ◽  
Contextual Information ◽  
Network Technology ◽  
Context Aware ◽  
Individual Privacy ◽  
Network Technologies

Incorporate contextual information into recommendation systems can obtain better accuracy of recommendation, however, the users’ individual privacy may be disclosed by attackers. In order to resolve this problem, the authors propose a context-aware recommendation system that integrates Differential Privacy and Bayesian Network technologies (DPBCF). Firstly, the paper uses k-means algorithm to cluster items to relieve sparsity of rating matrix. Next, for the sake of protecting users’ privacy, the paper adds Laplace noises to ratings. And then adopts Bayesian Network technology to calculate the probability that users like a type of item with contextual information. At last, the authors illustrate the experimental evaluations to show that the proposed algorithm can provide a stronger privacy protection while improving the accuracy of recommendations.

AI and privacy concerns: a smart meter case study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jillian Carmody ◽  
Samir Shringarpure ◽  
Gerhard Van de Venter
Keyword(s):  
Private Information ◽  
Personal Information ◽  
Consumer Information ◽  
Data Sets ◽  
Smart Meter ◽  
Smart Meters ◽  
Content Type ◽  
Privacy Concerns ◽  
Privacy Legislation

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.

scholarly journals Information Privacy Concerns Among Instagram Users: The Case of Indonesian College Students

2020 ◽  
Vol 6 (2) ◽  
pp. 159
Author(s):  
Eko Wahyu Tyas Darmaningrat ◽  
Hanim Maria Astuti ◽  
Fadhila Alfi
Keyword(s):  
College Students ◽  
Private Information ◽  
Structural Equation ◽  
Personal Information ◽  
Information Privacy ◽  
Risk Awareness ◽  
User Privacy ◽  
Privacy Concerns ◽  
Information Privacy Concerns ◽  
Increased Risk

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.

Exercising the Right of Privacy

2011 ◽  
pp. 1-28
Author(s):  
Scott Flinn ◽  
Scott Buffett
Keyword(s):  
Risk Management ◽  
Private Information ◽  
Information Exchange ◽  
Privacy Protection ◽  
Personal Information ◽  
Privacy Concerns ◽  
Right Of Privacy ◽  
Exposure Risks ◽  
The Right Of Privacy ◽  
The Right

This chapter discusses privacy from the perspective of the consumer of e-services. It proposes a technique for risk management assessment designed to help consumers evaluate a situation to identify and understand potential privacy concerns. The technique centers around a series of questions based on common principles of privacy protection. The chapter discusses how a consumer can understand exposure risks and how information can be controlled and monitored to mitigate the risks. It also proposes a method for assessing the consumer’s value of personal information, and a mechanism for automated negotiation is presented to facilitate fair, private information exchange. The authors believe that these or similar techniques are essential to give consumers of e-services meaningful control over the personal information they release. This forward-looking chapter provides a foundation for developing methods to empower users with control over their private information.

Why people need privacy? The role of privacy fatigue in app users' intention to disclose privacy: based on personality traits

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jie Tang ◽  
Umair Akram ◽  
Wenjing Shi
Keyword(s):  
Personality Traits ◽  
Information Disclosure ◽  
Personal Information ◽  
Mobile Apps ◽  
Equation Modeling ◽  
Content Type ◽  
Privacy Concerns ◽  
Positive Effects ◽  
Novel Concept ◽  
The Impact

PurposeMobile Applications (App) privacy has become a prominent social problem. Compared with privacy concerns, this study examines a relatively novel concept of privacy fatigue and explores its effect on the users’ intention to disclose their personal information via mobile Apps. In addition, the personality traits are proposed as antecedents that will induce the personal perception of privacy fatigue and privacy concerns differently.Design/methodology/approachData were collected from 426 respondents. Structure equation modeling was used to test the hypotheses.FindingsThe findings describe that App users’ intention toward personal information disclosure is determined by privacy fatigue and privacy concerns, but the former has a greater impact. With minor exceptions, the two factors are also influenced by different personality traits. Specifically, neuroticism has positive effects on privacy fatigue, but agreeableness and extraversion have presented the opposite results on the two variables.Practical implicationsThis research is very scarce to examine the joint effects of privacy fatigue, privacy concerns and personality traits on App users’ disclosing intention. In doing so, these results will be of benefit to App providers and platform managers and can be the basis for a variety of follow-up studies.Originality/valueWhile previous research just focuses on privacy concerns, this study explores the critical roles of privacy fatigue and opens up a new avenue of emotion-attitude analysis that can further increase the specificity and richness of users’ privacy research. Additionally, implications for personality traits as antecedents in the impact of App users’ privacy emotions and attitudes are discussed.

Triggers and motivators of privacy protection behavior on Facebook

2019 ◽  
Vol 33 (1) ◽  
pp. 57-72 ◽  
Author(s):  
Linda Alkire ◽  
Johannes Pohlmann ◽  
Willy Barnett
Keyword(s):  
Social Media ◽  
Privacy Protection ◽  
Well Being ◽  
Social Recognition ◽  
Research Approach ◽  
Exploratory Research ◽  
User Privacy ◽  
Content Type ◽  
Area Of Interest ◽  
The Impact

Purpose Internet user privacy risks have been a topical subject with respect to consumers, corporations and governments. In line with the recent privacy scandals linked to social media, the aim of this study is to explore users’ privacy protection behaviors (PPB) on Facebook through the actions they take to protect their privacy, their underlying motives and the values behind these protective actions. Moreover, this study aims to address an unintended consequence of Facebook usage. Despite Facebook’s positive and uplifting goal of connecting people, consumers are forced to resort to specific behaviors to protect their privacy and well-being. Design/methodology/approach This study adopts an exploratory research approach by using a well-established qualitative technique: structured laddering interviews. In total, 20 in-depth personal interviews were conducted with the Millennials. Findings Results show that the process of privacy protection is initiated by experiences, uncertainty and literacy, rather than threats, which leads to concerns that trigger PPBs. The most common PPBs include: “Reflection,” “Avoidance,” “Intervention,” “Restriction,” “Control,” and “Restraint.” The underlying motives for the adoption of these strategies include: “Success,” “Security,” “Social Recognition,” “A World of Peace,” “Exclusivity of Self,” “Being in Control,” “Meaning” and “True Friendship”. Originality/value The present research adopts a transdisciplinary framework to help fill the gap regarding the interplay of PPBs on Facebook, the triggers of those behaviors and their underlying motives. It contributes to the service literature and practice as it provides insights into a growing area of interest, whereas more social media channels are being created and more services are using social media strategies to engage and interact with their customers. Finally, it addresses the growing need to consider the impact of technological services, including internet and social media, on consumers’ and societies’ well-being.

Modeling guests’ intentions to use mobile apps in hotels

2016 ◽  
Vol 28 (9) ◽  
pp. 1968-1991 ◽  
Author(s):  
Cristian Morosan ◽  
Agnes DeFranco
Keyword(s):  
Information Systems ◽  
Structural Model ◽  
Personal Information ◽  
Mobile Apps ◽  
Theoretical Perspective ◽  
Critical Factor ◽  
Structural Equations ◽  
Content Type ◽  
Privacy Concerns ◽  
The Usa

Purpose The unprecedented development of hotel-branded mobile applications (apps) has been instrumental in facilitating the rich guest–hotel interactions, thus contributing to a high personalization of services. For true personalization, guests need to provide personal information via apps. Yet, no study to date has addressed how guests develop intentions to use such apps given the current personalization and privacy challenges. Therefore, this study aims to investigate hotel guests’ intentions to use hotel apps to access personalized services. Design/methodology/approach Drawing from personalization-privacy theory, this study conceptualized perceived personalization and privacy concerns as distinct constructs while recognizing two different privacy concerns constructs: general and app-specific privacy concerns. To build a comprehensive structural model that is appropriate for explicating intentions to use hotel apps, this study incorporates consumer psychology and information systems theoretical streams that provide constructs that unequivocally capture the unique set of consumer–app interactions in highly experiential settings such as hotels (e.g. innovativeness and involvement). Using a nation-wide sample of hotel guests from the USA, the model was validated using confirmatory factor analysis and structural equations modeling. Findings The predictors explained 79 per cent of the variability in the intentions to use hotel apps to personalize hotel services. The strongest predictor of intentions was involvement, followed by app-related privacy concerns and perceived personalization. Research limitations/implications First, this study’s extended theoretical framework was well supported, as it captures relevant elements of the mobile commerce ecosystem (e.g. personalization and privacy), thus extending the classic paradigmatic approach to information systems adoption beyond system beliefs. Second, this study clarifies the distinct roles of personalization and privacy in the context of hotel apps, which has not been examined in the context of m-commerce in hospitality. Third, the study clarifies the role of involvement as the most critical factor that can influence guests’ intentions to use hotel apps when personalization options and privacy concerns exist. Practical implications This study offers hotel decision-makers a mapping of the factors, leading to use of hotel apps for purchasing personalized hotel services. Originality/value This study provides a first theoretical perspective on the hotel app utilization behaviors that have not been studied so far, but carry a strong strategic and financial significance for the hotel industry (direct distribution, brand consolidation and extensive contact with guests).

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