A Survey on Privacy Preservation in Location-Based Mobile Business

2021 ◽  
Vol 13 (1) ◽  
pp. 20-39
Author(s):  
Ahmed Aloui ◽  
Okba Kazar
Keyword(s):  
Privacy Protection ◽  
Privacy Preservation ◽  
Service Providers ◽  
Personal Information ◽  
Third Party ◽  
Mobile Business ◽  
Protection Measures ◽  
System Architectures ◽  
Protection Goals ◽  
Privacy Issues

In mobile business (m-business), a client sends its exact locations to service providers. This data may involve sensitive and private personal information. As a result, misuse of location information by the third party location servers creating privacy issues for clients. This paper provides an overview of the privacy protection techniques currently applied by location-based mobile business. The authors first identify different system architectures and different protection goals. Second, this article provides an overview of the basic principles and mechanisms that exist to protect these privacy goals. In a third step, the authors provide existing privacy protection measures.

scholarly journals An Empirical Analysis of Consumers’ Perceptions of Privacy Protection

2021 ◽  
pp. 13-33
Author(s):  
Jagdish N. Sambada ◽  
Sanjay Bhayani
Keyword(s):  
Social Networking ◽  
Marital Status ◽  
Privacy Protection ◽  
Service Providers ◽  
Demographic Variable ◽  
Personal Information ◽  
Sampling Technique ◽  
Chi Square ◽  
Social Networking Websites ◽  
Privacy Issues

This study explores how consumers perceive privacy protection and how their perceptions differ by different demographic factors like gender, profession, education and marital status. The study focuses on one of the dimensions of privacy which is privacy protection. In India studies have been done to understand consumer attitudes and concern towards privacy issues but relationship between demographic variables and consumers’ perception of privacy issues have not been researched. The descriptive research design is being used to study the formulated problem. Non-Probability convenient Random sampling technique has been used for research. Chi-square test has been used to test the relationship as the variables under study are categorical. Findings of the study revealed that when it comes to safety of personal information on smartphones profession as a demographic variable has significant effect on consumers’ perception of privacy protection. Marital status as a demographic variable also has effect on consumers’ perception of privacy protection on social networking websites. Married respondents are seen to be more apprehensive of their privacy protection on social networking websites. As most of the customers do not hesitate in sharing their personal information with service providers it has positive implications for the companies.

scholarly journals Privacy preservation Models for Third-Party Auditor over Cloud Computing: a Survey

2021 ◽  
Author(s):  
Abdul Razaque ◽  
Mohamed Frej ◽  
Bandar Alotaibi ◽  
Munif Alotaibi
Keyword(s):  
Cloud Computing ◽  
Privacy Preservation ◽  
Service Providers ◽  
Personal Information ◽  
Cloud Service ◽  
Privacy Preserving ◽  
Third Party ◽  
Security Requirements ◽  
Security Threats ◽  
State Of Art

Cloud computing has become a prominent technology due to its important utility service; this service concentrates on outsourcing data to organizations and individual consumers. Cloud computing has considerably changed the manner in which individuals or organizations store, retrieve, and organize their personal information. Despite the manifest development in cloud computing, there are still some concerns regarding the level of security and issues related to adopting cloud computing that prevent users from fully trusting this useful technology. Hence, for the sake of reinforcing the trust between Cloud Clients (CC) and Cloud Service Providers (CSP), as well as safeguarding the CC’s data in the cloud, several security paradigms of cloud computing based on a Third-Party Auditor (TPA) have been introduced. The TPA, as a trusted party, is responsible for checking the integrity of the CC’s data and all the critical information associated with it. However, the TPA could become an adversary and could aim to deteriorate the privacy of the CC’s data by playing a malicious role. In this paper, we present the state-of-art of cloud computing’s privacy-preserving models (PPM) based on a TPA. Three TPA factors of paramount significance have been discussed: TPA involvement, security requirements, and security threats caused by vulnerabilities. Moreover, TPA’s privacy preserving models have been comprehensively analyzed and categorized into different classes with an emphasis on their dynamicity. Finally, we discuss the limitations of the models and present our recommendations for their improvement.

scholarly journals Protecting the Moving User’s Locations by Combining Differential Privacy and k -Anonymity under Temporal Correlations in Wireless Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Weiqi Zhang ◽  
Guisheng Yin ◽  
Yuhai Sha ◽  
Jishen Yang
Keyword(s):  
Privacy Protection ◽  
Differential Privacy ◽  
Service Providers ◽  
Personal Information ◽  
Rapid Development ◽  
High Standard ◽  
Location Based Services ◽  
Third Party ◽  
The Third ◽  
Temporal Correlations

The rapid development of the Global Positioning System (GPS) devices and location-based services (LBSs) facilitates the collection of huge amounts of personal information for the untrusted/unknown LBS providers. This phenomenon raises serious privacy concerns. However, most of the existing solutions aim at locating interference in the static scenes or in a single timestamp without considering the correlation between location transfer and time of moving users. In this way, the solutions are vulnerable to various inference attacks. Traditional privacy protection methods rely on trusted third-party service providers, but in reality, we are not sure whether the third party is trustable. In this paper, we propose a systematic solution to preserve location information. The protection provides a rigorous privacy guarantee without the assumption of the credibility of the third parties. The user’s historical trajectory information is used as the basis of the hidden Markov model prediction, and the user’s possible prospective location is used as the model output result to protect the user’s trajectory privacy. To formalize the privacy-protecting guarantee, we propose a new definition, L&A-location region, based on k -anonymity and differential privacy. Based on the proposed privacy definition, we design a novel mechanism to provide a privacy protection guarantee for the users’ identity trajectory. We simulate the proposed mechanism based on a dataset collected in real practice. The result of the simulation shows that the proposed algorithm can provide privacy protection to a high standard.

scholarly journals Privacy Preservation Models for Third-Party Auditor over Cloud Computing: A Survey

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2721
Author(s):  
Abdul Razaque ◽  
Mohamed Ben Haj Frej ◽  
Bandar Alotaibi ◽  
Munif Alotaibi
Keyword(s):  
Cloud Computing ◽  
Privacy Preservation ◽  
Service Providers ◽  
State Of The Art ◽  
Personal Information ◽  
Cloud Service ◽  
Privacy Preserving ◽  
Third Party ◽  
Security Requirements ◽  
Security Threats

Cloud computing has become a prominent technology due to its important utility service; this service concentrates on outsourcing data to organizations and individual consumers. Cloud computing has considerably changed the manner in which individuals or organizations store, retrieve, and organize their personal information. Despite the manifest development in cloud computing, there are still some concerns regarding the level of security and issues related to adopting cloud computing that prevent users from fully trusting this useful technology. Hence, for the sake of reinforcing the trust between cloud clients (CC) and cloud service providers (CSP), as well as safeguarding the CC’s data in the cloud, several security paradigms of cloud computing based on a third-party auditor (TPA) have been introduced. The TPA, as a trusted party, is responsible for checking the integrity of the CC’s data and all the critical information associated with it. However, the TPA could become an adversary and could aim to deteriorate the privacy of the CC’s data by playing a malicious role. In this paper, we present the state of the art of cloud computing’s privacy-preserving models (PPM) based on a TPA. Three TPA factors of paramount significance are discussed: TPA involvement, security requirements, and security threats caused by vulnerabilities. Moreover, TPA’s privacy preserving models are comprehensively analyzed and categorized into different classes with an emphasis on their dynamicity. Finally, we discuss the limitations of the models and present our recommendations for their improvement.

scholarly journals Decentralized collaborative TTP free approach for privacy preservation in location based services

2019 ◽  
Vol 9 (6) ◽  
pp. 5376
Author(s):  
Ajaysinh Devendrasinh Rathod ◽  
Saurabh Shah ◽  
Vivaksha J. Jariwala
Keyword(s):  
Privacy Preservation ◽  
Location Privacy ◽  
Emergency Services ◽  
Personal Information ◽  
Personal Digital Assistant ◽  
Minimum Cost ◽  
Location Based Services ◽  
Third Party ◽  
Trusted Third Party ◽  
Recent Trends

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.

Security Aspects in Cloud Computing

2018 ◽  
pp. 54-76
Author(s):  
Tabassum N. Mujawar ◽  
Ashok V. Sutagundar ◽  
Lata L. Ragha
Keyword(s):  
Cloud Computing ◽  
Service Providers ◽  
Cloud Service ◽  
Third Party ◽  
Storage Security ◽  
Security Issues ◽  
Access Control Mechanism ◽  
And Storage ◽  
Privacy Issues ◽  
Different Levels

Cloud computing is recently emerging technology, which provides a way to access computing resources over Internet on demand and pay per use basis. Cloud computing is a paradigm that enable access to shared pool of resources efficiently, which are managed by third party cloud service providers. Despite of various advantages of cloud computing security is the biggest threat. This chapter describes various security concerns in cloud computing. The clouds are subject to traditional data confidentiality, integrity, availability and various privacy issues. This chapter comprises various security issues at different levels in environment that includes infrastructure level security, data level and storage security. It also deals with the concept of Identity and Access Control mechanism.

scholarly journals KLPPS: A k-Anonymous Location Privacy Protection Scheme via Dummies and Stackelberg Game

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Dongdong Yang ◽  
Baopeng Ye ◽  
Wenyin Zhang ◽  
Huiyu Zhou ◽  
Xiaobin Qian
Keyword(s):  
Service Quality ◽  
Privacy Protection ◽  
Privacy Preservation ◽  
Location Privacy ◽  
Stackelberg Game ◽  
Single Point ◽  
Third Party ◽  
Protection Scheme ◽  
Inference Attacks ◽  
Location Privacy Protection

Protecting location privacy has become an irreversible trend; some problems also come such as system structures adopted by location privacy protection schemes suffer from single point of failure or the mobile device performance bottlenecks, and these schemes cannot resist single-point attacks and inference attacks and achieve a tradeoff between privacy level and service quality. To solve these problems, we propose a k-anonymous location privacy protection scheme via dummies and Stackelberg game. First, we analyze the merits and drawbacks of the existing location privacy preservation system architecture and propose a semitrusted third party-based location privacy preservation architecture. Next, taking into account both location semantic diversity, physical dispersion, and query probability, etc., we design a dummy location selection algorithm based on location semantics and physical distance, which can protect users’ privacy against single-point attack. And then, we propose a location anonymous optimization method based on Stackelberg game to improve the algorithm. Specifically, we formalize the mutual optimization of user-adversary objectives by using the framework of Stackelberg game to find an optimal dummy location set. The optimal dummy location set can resist single-point attacks and inference attacks while effectively balancing service quality and location privacy. Finally, we provide exhaustive simulation evaluation for the proposed scheme compared with existing schemes in multiple aspects, and the results show that the proposed scheme can effectively resist the single-point attack and inference attack while balancing the service quality and location privacy.

scholarly journals Privacy Threats and Protection Recommendations for the Use of Geosocial Network Data in Research

2018 ◽  
Vol 7 (10) ◽  
pp. 191 ◽  
Author(s):  
Ourania Kounadi ◽  
Bernd Resch ◽  
Andreas Petutschnig
Keyword(s):  
Social Media ◽  
Personal Information ◽  
Network Data ◽  
Protection Measures ◽  
Individual Privacy ◽  
Inference Attacks ◽  
Scientific Results ◽  
The One ◽  
Privacy Issues ◽  
Media Data

Inference attacks and protection measures are two sides of the same coin. Although the former aims to reveal information while the latter aims to hide it, they both increase awareness regarding the risks and threats from social media apps. On the one hand, inference attack studies explore the types of personal information that can be revealed and the methods used to extract it. An additional risk is that geosocial media data are collected massively for research purposes, and the processing or publication of these data may further compromise individual privacy. On the other hand, consistent and increasing research on location protection measures promises solutions that mitigate disclosure risks. In this paper, we examine recent research efforts on the spectrum of privacy issues related to geosocial network data and identify the contributions and limitations of these research efforts. Furthermore, we provide protection recommendations to researchers that share, anonymise, and store social media data or publish scientific results.

scholarly journals Privacy and E-Learning: A Pending Task

2021 ◽  
Vol 13 (16) ◽  
pp. 9206
Author(s):  
Marc Alier ◽  
Maria Jose Casañ Guerrero ◽  
Daniel Amo ◽  
Charles Severance ◽  
David Fonseca
Keyword(s):  
Educational Software ◽  
Service Providers ◽  
Personal Information ◽  
Management Strategies ◽  
Third Party ◽  
Learning Tools ◽  
Software Programs ◽  
Computing Platforms ◽  
Data Surveillance ◽  
Learning Institutions

Most educational software programs use and gather personal information and metadata from students. Additionally, most of the educational software programs are no longer operated by the learning institutions but are run by third-party agencies. This means that in the decade since 2020, information about students is stored and handled outside premises and control of learning institutions. The personal information about students and their activity while they interact with learning management systems and online learning tools is increasingly in custody of cloud computing platforms, software-as-a-service providers, and learning tool vendors. There is an increasing will to use all the data and metadata from the activity of the students for research, to develop education management strategies, pedagogy approaches, and develop behavior control tools or learning tools informed by behavior analysis from learning analytics. Many times, these studies lack the ethical and moral perspective. In addition, there is an increasing number of cases in which this information has leaked or has been used in a shady way. Additionally, this information will be around for a long time, tied to the future digital profiles of the students whose data has been leaked. This paper hypothesizes that there has been an ongoing process of technological evolution that leads to a loss of control over personal information, which makes it even more difficult to protect user confidentiality and ensuring privacy, that data surveillance has entered the world of education, and that the current legal frameworks are not enough to really protect the student’s personal information. The paper analyzes how this situation came to pass, and why this is wrong. We conclude with some proposals to address it from its different root dimensions: technical, cultural, legal, and organizational.

scholarly journals Internet Service Providers as Privacy Custodians

2018 ◽  
Vol 33 (3) ◽  
pp. 401-423
Author(s):  
Mike Zajko
Keyword(s):  
Privacy Protection ◽  
Service Providers ◽  
Personal Information ◽  
Sensitive Information ◽  
Internet Service Providers ◽  
Positive Orientation ◽  
Internet Service ◽  
Legal Obligations ◽  
Highly Sensitive

AbstractThis article examines the role of internet service providers (ISPs) as guardians of personal information and protectors of privacy, with a particular focus on how telecom companies in Canada have historically negotiated these responsibilities. Communications intermediaries have long been expected to act as privacy custodians by their users, while simultaneously being subject to pressures to collect, utilize, and disclose personal information. As service providers gain custody over increasing volumes of highly-sensitive information, their importance as privacy custodians has been brought into starker relief and explicitly recognized as a core responsibility.Some ISPs have adopted a more positive orientation to this responsibility, actively taking steps to advance it, rather that treating privacy protection as a set of limitations on conduct. However, commitments to privacy stewardship are often neutralized through contradictory legal obligations (such as mandated surveillance access) and are recurrently threatened by commercial pressures to monetize personal information.

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