3 research outputs found
Obfuscation and anonymization methods for locational privacy protection : a systematic literature review
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe mobile technology development combined with the business model of a majority
of application companies is posing a potential risk to individuals’ privacy.
Because the industry default practice is unrestricted data collection. Although,
the data collection has virtuous usage in improve services and procedures; it also
undermines user’s privacy. For that reason is crucial to learn what is the privacy
protection mechanism state-of-art.
Privacy protection can be pursued by passing new regulation and developing
preserving mechanism. Understanding in what extent the current technology is
capable to protect devices or systems is important to drive the advancements
in the privacy preserving field, addressing the limits and challenges to deploy
mechanism with a reasonable quality of Service-QoS level.
This research aims to display and discuss the current privacy preserving
schemes, its capabilities, limitations and challenges
From Data Flows to Privacy-Benefit Trade-offs: A User-Centric Semantic Model
In today's highly connected cyber-physical world, people are constantly disclosing personal and sensitive data to different organizations and other people through the use of online and physical services. This is because sharing personal information can bring various benefits for themselves and others. However, data disclosure activities can lead to unexpected privacy issues, and there is a general lack of tools that help to improve users' awareness of the subtle privacy-benefit trade-offs and to make more informed decisions on their data disclosure activities in wider contexts. To fill this gap, this paper presents a novel user-centric, data-flow graph based semantic model, which can show how a given user's personal and sensitive data have been disclosed to different entities and what benefits the user gained through such data disclosures. The model allows automatic analysis of privacy-benefit trade-offs around a target user's data sharing activities, therefore it can support development of user-centric software tools for people to better manage their data disclosure activities to achieve a better balance between privacy and benefits in the cyber-physical world
A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions
In recent decades, social network anonymization has become a crucial research
field due to its pivotal role in preserving users' privacy. However, the high
diversity of approaches introduced in relevant studies poses a challenge to
gaining a profound understanding of the field. In response to this, the current
study presents an exhaustive and well-structured bibliometric analysis of the
social network anonymization field. To begin our research, related studies from
the period of 2007-2022 were collected from the Scopus Database then
pre-processed. Following this, the VOSviewer was used to visualize the network
of authors' keywords. Subsequently, extensive statistical and network analyses
were performed to identify the most prominent keywords and trending topics.
Additionally, the application of co-word analysis through SciMAT and the
Alluvial diagram allowed us to explore the themes of social network
anonymization and scrutinize their evolution over time. These analyses
culminated in an innovative taxonomy of the existing approaches and
anticipation of potential trends in this domain. To the best of our knowledge,
this is the first bibliometric analysis in the social network anonymization
field, which offers a deeper understanding of the current state and an
insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure