PISA: A proximity-based social networking (PBSN) protection model

The widespread adoption of Proximity-based Social Networking (PBSN) applications has been accompanied with several privacy concerns involving location information. As a result, many studies were directed towards innovative privacy-preserving solutions to provide a secure platform for mobile users. D...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Security journal Jg. 36; H. 1; S. 165 - 200
Hauptverfasser: Ramtohul, Asslinah Mocktoolah, Khedo, Kavi Kumar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Palgrave Macmillan UK 01.03.2023
Palgrave Macmillan
Schlagworte:
Algorithms
Computer privacy
Crime and Society
Criminology and Criminal Justice
Evaluation
Measurement
Networking
Original Article
Privacy
Property
Protection
Proximity
Research methodology
Security
Social networks
Social Sciences
Welfare state
Privacy assessment
Proximity-based social networking
Location-based systems
Evaluation metrics
Security analysis
ISSN:0955-1662, 1743-4645
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The widespread adoption of Proximity-based Social Networking (PBSN) applications has been accompanied with several privacy concerns involving location information. As a result, many studies were directed towards innovative privacy-preserving solutions to provide a secure platform for mobile users. Despite the success of these solutions, there is a research gap in terms of the evaluation and analysis of their protection features. An in-depth evaluation of the privacy and security provisions in PBSN systems is necessary to assess the protection properties. In this paper, a comprehensive protection assessment model, the PISA model, is proposed to evaluate the privacy and security features of PBSN frameworks. The main objectives of this study refer to defining the protection goals of PBSN systems by reviewing the privacy and security requirements, analyzing the associated location privacy threats, and formulating the PISA model based on the quantification of the related protection goals using the privacy metrics. The study adopts an exploratory research methodology and explores four distinct research questions. The PISA model enables an extensive evaluation of privacy-preserving PBSN frameworks concerning their privacy and security features which can be further useful for researchers during the development of privacy-preserving algorithms to prevent flaws in advance and improve where necessary. Future works of the current research can focus on the analysis of privacy policies and adversary models based on their assumptions, resources, and capabilities.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0955-1662
1743-4645
DOI:10.1057/s41284-022-00334-5