A Privacy-Preserving Efficient Location-Sharing Scheme for Mobile Online Social Network Applications

Bhattacharya, Munmun; Roy, Sandip; Mistry, Kamlesh; Shum, Hubert P. H.; Chattopadhyay, Samiran

doi:10.1109/access.2020.3043621

Cited by 8 publications

(3 citation statements)

References 55 publications

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“…Attackers track and collect the continuous location of the user for the time they are using those applications. 80 From this data, attackers can get the whereabouts of the victim user, demographic information, and identify the OSN user physically. 81…”

Section: Identity Related Attacksmentioning

confidence: 99%

“…Instead of disclosing the users' current GPS location, WeChat, Skout, such OSNs show the distance between OSN friends. Attackers track and collect the continuous location of the user for the time they are using those applications 80 . From this data, attackers can get the whereabouts of the victim user, demographic information, and identify the OSN user physically 81…”

Section: Privacy and Security Threats In Osnsmentioning

confidence: 99%

See 1 more Smart Citation

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

Bhattacharya

Roy

Chattopadhyay

et al. 2022

Security and Privacy

Self Cite

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Over the past few years, online social networks (OSNs) have become an inseparable part of people's daily lives. Instead of being passive readers, people are now enjoying their role as content contributors. OSN has permitted its users to share their information including the multimedia content. OSN users can express themselves in virtual communities by providing their opinions and interacting with others. As a consequence, the privacy and security threats in OSNs have emerged as a major concern to the research and business world. In the recent past, a number of survey works have been conducted to discuss different security and privacy threats in OSNs. However, till date, no survey work has been conducted that aims to classify and analyze various machine learning (ML)‐based solutions adapted for the security defense of OSNs. In this survey article, we present a detailed taxonomy with a classification of various works done on various security attacks in OSNs. We then review and summarize the existing state of art survey works on OSN security, and indicate the merits and limitations of these survey works. Next, we review all recent works that aim to provide ML‐based solutions toward defense of security attacks on OSNs. Finally, we discuss the future road‐map on OSN security and provide a comprehensive analysis on the open research issues with feasible measurements and possible solutions.

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Section: Identity Related Attacksmentioning

confidence: 99%

Section: Privacy and Security Threats In Osnsmentioning

confidence: 99%

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

Bhattacharya

Roy

Chattopadhyay

et al. 2022

Security and Privacy

Self Cite

View full text Add to dashboard Cite

show abstract

“…Individuals utilize Online Social Network (OSN) platforms to interact and communicate for several purposes, such as marketing, entertainment, and business. However, sharing extensive personal details, including ideas, photos, videos, events, and personal information, can expose users to exploitation and privacy concerns [8], [9]. Moreover, the emergence of COVID-19 has led to a shift in the way people live and work, with many organizations moving to remote work and online platforms to continue their operations.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Taxonomy of Social Engineering Attacks and Defense Mechanisms: Toward Effective Mitigation Strategies

Zaoui,

Yousra,

Yassine

et al. 2024

IEEE Access

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Social engineering (SE) attacks are a growing concern for organizations that rely on technology to protect sensitive data. Identifying and preventing these attacks can be challenging, as they frequently rely on manipulating human behavior rather than exploiting technical vulnerabilities. Although various studies have explored SE attacks and their defense mechanisms, there remains a gap in the literature concerning the holistic and layered classification of these threats and countermeasures. To address this, we conducted a comprehensive literature survey to understand existing taxonomies and subsequently identified areas that required a more structured and exhaustive categorization. Based on the survey results, we propose a comprehensive taxonomy of SE attacks, classifying them based on three levels: environment, approaches, and mediums. Additionally, we present a taxonomy of social engineering countermeasures, encompassing both technical and non-technical solutions. The proposed taxonomies serve as a foundation for future research and offer organizations a valuable framework for developing effective strategies to detect, prevent, and respond to social engineering incidents.

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Assessing the impact of geo-targeted warning messages on residents’ evacuation decisions before a hurricane using agent-based modeling

Gao

Wang

2021

Nat Hazards

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A Privacy-Preserving Efficient Location-Sharing Scheme for Mobile Online Social Network Applications

Cited by 8 publications

References 55 publications

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

A Comprehensive Taxonomy of Social Engineering Attacks and Defense Mechanisms: Toward Effective Mitigation Strategies

Assessing the impact of geo-targeted warning messages on residents’ evacuation decisions before a hurricane using agent-based modeling

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