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 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 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.

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.

scholarly journals Privacy in targeted advertising: A survey

2020 ◽  
Author(s):  
Imdad Ullah ◽  
Roksana Boreli ◽  
Salil S. Kanhere
Keyword(s):  
Private Information ◽  
Personal Data ◽  
Third Party ◽  
User Privacy ◽  
Research Issues ◽  
Privacy Concerns ◽  
Targeted Advertising ◽  
Privacy Threats ◽  
Measurement Analysis ◽  
Privacy Risks

Targeted advertising has transformed the marketing trend for any business by creating new opportunities for advertisers to reach prospective customers by delivering them personalised ads using an infrastructure of a variety of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process and trade a rich amount of user's personal data, which has prompted serious privacy concerns among individuals and organisations. This article presents a detailed survey of privacy risks including the information flow between advertising platform and ad/analytics networks, the profiling process, the advertising sources and criteria, the measurement analysis of targeted advertising based on user's interests and profiling context and ads delivery process in both in-app and in-browser targeted ads. We provide detailed discussion of challenges in preserving user privacy that includes privacy threats posed by the advertising and analytics companies, how private information is extracted and exchanged among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks, in addition to, overview data and tracking sharing technologies. Following, we present various techniques for preserving user privacy and a comprehensive analysis of various proposals founded on those techniques and compare them based on the underlying architectures, the privacy mechanisms and the deployment scenarios. Finally we discuss some potential research challenges and open research issues.<br>

scholarly journals Privacy in targeted advertising: A survey

2020 ◽  
Author(s):  
Imdad Ullah ◽  
Roksana Boreli ◽  
Salil S. Kanhere
Keyword(s):  
Private Information ◽  
Personal Data ◽  
Third Party ◽  
User Privacy ◽  
Research Issues ◽  
Privacy Concerns ◽  
Targeted Advertising ◽  
Privacy Threats ◽  
Measurement Analysis ◽  
Privacy Risks

Targeted advertising has transformed the marketing trend for any business by creating new opportunities for advertisers to reach prospective customers by delivering them personalised ads using an infrastructure of a variety of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process and trade a rich amount of user's personal data, which has prompted serious privacy concerns among individuals and organisations. This article presents a detailed survey of privacy risks including the information flow between advertising platform and ad/analytics networks, the profiling process, the advertising sources and criteria, the measurement analysis of targeted advertising based on user's interests and profiling context and ads delivery process in both in-app and in-browser targeted ads. We provide detailed discussion of challenges in preserving user privacy that includes privacy threats posed by the advertising and analytics companies, how private information is extracted and exchanged among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks, in addition to, overview data and tracking sharing technologies. Following, we present various techniques for preserving user privacy and a comprehensive analysis of various proposals founded on those techniques and compare them based on the underlying architectures, the privacy mechanisms and the deployment scenarios. Finally we discuss some potential research challenges and open research issues.<br>

scholarly journals A Mobile, Dynamic, and Privacy-Preserving Matching System for Car and Taxi Pools

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Shin-Yan Chiou ◽  
Yi-Cheng Chen
Keyword(s):  
Private Information ◽  
Traffic Congestion ◽  
Ad Hoc ◽  
Personal Data ◽  
Vehicle Ownership ◽  
User Privacy ◽  
Computing Systems ◽  
Privacy Concerns ◽  
Dynamic Matching ◽  
Registration Data

Recently, rates of vehicle ownership have risen globally, exacerbating problems including air pollution, lack of parking, and traffic congestion. While many solutions to these problems have been proposed, carpooling remains one of the most effective approaches. Recently, several carpooling platforms have been built on cloud computing systems, with originators posting online list of departure/arrival points and schedules from which participants can search for rides that match their needs. However, it can be difficult to make matches quickly and the systems are subject to privacy concerns in that they may disclose private information such as names, registration data, and departure/arrival schedules. This paper proposes a dynamic matching method for car/taxi pools for use in mobile devices via ad hoc Wi-Fi networks. The proposed method also preserves user privacy including names and departure/arrival schedules. Moreover, the system does not require the user to register any personal data, so such data cannot be leaked. The system was implemented on the Android mobile platform, allowing users to immediately and securely access the system via their smart phones.

scholarly journals Privacy in targeted advertising: A survey

2020 ◽  
Author(s):  
Imdad Ullah ◽  
Roksana Boreli ◽  
Salil S. Kanhere
Keyword(s):  
Private Information ◽  
Personal Data ◽  
Third Party ◽  
User Privacy ◽  
Research Issues ◽  
Privacy Concerns ◽  
Targeted Advertising ◽  
Privacy Threats ◽  
Measurement Analysis ◽  
Privacy Risks

Targeted advertising has transformed the marketing trend for any business by creating new opportunities for advertisers to reach prospective customers by delivering them personalised ads using an infrastructure of a variety of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process and trade a rich amount of user's personal data, which has prompted serious privacy concerns among individuals and organisations. This article presents a detailed survey of privacy risks including the information flow between advertising platform and ad/analytics networks, the profiling process, the advertising sources and criteria, the measurement analysis of targeted advertising based on user's interests and profiling context and ads delivery process in both in-app and in-browser targeted ads. We provide detailed discussion of challenges in preserving user privacy that includes privacy threats posed by the advertising and analytics companies, how private information is extracted and exchanged among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks, in addition to, overview data and tracking sharing technologies. Following, we present various techniques for preserving user privacy and a comprehensive analysis of various proposals founded on those techniques and compare them based on the underlying architectures, the privacy mechanisms and the deployment scenarios. Finally we discuss some potential research challenges and open research issues.<br>

Privacy and Trust Issues in Context-Aware Pervasive Computing

2010 ◽  
pp. 352-377 ◽  
Author(s):  
Pierre E. Abi-Char ◽  
Bachar El-Hassan ◽  
Mounir Mokhtari
Keyword(s):  
Pervasive Computing ◽  
Context Awareness ◽  
Contextual Information ◽  
Mobile Technologies ◽  
Security Requirements ◽  
Security And Privacy ◽  
Context Aware ◽  
User Privacy ◽  
Privacy And Security ◽  
Information And Communication

The growing evolution of information and communication technology (ICT) systems towards more pervasive and ubiquitous infrastructures contribute significantly to the deployment of services anywhere, at anytime and for anyone. To provide personalized services in such infrastructures, we should consider both user’s privacy and security requirements and context-awareness environment. This can be really achieved owing to context awareness systems which allow us to benefit from sensing and mobile technologies to derive more accurate data about the user and his/her location. While the availability of contextual information may introduce new threats against security and privacy, it can also be used to improve dynamic, adaptive and autonomic aspects of security, and user privacy. Moreover, context-aware information offers new opportunities for the establishment of trust relationship among involved entities (e.g., users, devices, and platforms). As context awareness represents new challenges and new opportunities regarding privacy, trust and security of users in pervasive computing environments (PCE), the main purpose of this chapter aims to survey each of the involved issues to understand and address the interdependencies among them.

Minimizing Bias in Forensic Handwriting Examinations

2015 ◽  
Vol 25 ◽  
pp. 17-26 ◽  
Author(s):  
L. C. Alewijnse ◽  
E.J.A.T. Mattijssen ◽  
R.D. Stoel
Keyword(s):  
Decision Making ◽  
Information Management ◽  
Contextual Information ◽  
Context Information ◽  
Decision Making Process ◽  
Potential Bias ◽  
Potential Influence ◽  
Management Procedures

The purpose of this paper is to contribute to the increasing awareness about the potential bias on the interpretation and conclusions of forensic handwriting examiners (FHEs) by contextual information. We briefly provide the reader with an overview of relevant types of bias, the difficulties associated with studying bias, the sources of bias and their potential influence on the decision making process in casework, and solutions to minimize bias in casework. We propose that the limitations of published studies on bias need to be recognized and that their conclusions must be interpreted with care. Instead of discussing whether bias is an issue in casework, the forensic handwriting community should actually focus on how bias can be minimized in practice. As some authors have already shown (e.g., Found & Ganas, 2014), it is relatively easy to implement context information management procedures in practice. By introducing appropriate procedures to minimize bias, not only forensic handwriting examination will be improved, it will also increase the acceptability of the provided evidence during court hearings. Purchase Article - $10

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