885 research outputs found
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
An efficient parallel method for mining frequent closed sequential patterns
Mining frequent closed sequential pattern (FCSPs) has attracted a great deal of research attention, because it is an important task in sequences mining. In recently, many studies have focused on mining frequent closed sequential patterns because, such patterns have proved to be more efficient and compact than frequent sequential patterns. Information can be fully extracted from frequent closed sequential patterns. In this paper, we propose an efficient parallel approach called parallel dynamic bit vector frequent closed sequential patterns (pDBV-FCSP) using multi-core processor architecture for mining FCSPs from large databases. The pDBV-FCSP divides the search space to reduce the required storage space and performs closure checking of prefix sequences early to reduce execution time for mining frequent closed sequential patterns. This approach overcomes the problems of parallel mining such as overhead of communication, synchronization, and data replication. It also solves the load balance issues of the workload between the processors with a dynamic mechanism that re-distributes the work, when some processes are out of work to minimize the idle CPU time.Web of Science5174021739
Private Graph Data Release: A Survey
The application of graph analytics to various domains have yielded tremendous
societal and economical benefits in recent years. However, the increasingly
widespread adoption of graph analytics comes with a commensurate increase in
the need to protect private information in graph databases, especially in light
of the many privacy breaches in real-world graph data that was supposed to
preserve sensitive information. This paper provides a comprehensive survey of
private graph data release algorithms that seek to achieve the fine balance
between privacy and utility, with a specific focus on provably private
mechanisms. Many of these mechanisms fall under natural extensions of the
Differential Privacy framework to graph data, but we also investigate more
general privacy formulations like Pufferfish Privacy that can deal with the
limitations of Differential Privacy. A wide-ranging survey of the applications
of private graph data release mechanisms to social networks, finance, supply
chain, health and energy is also provided. This survey paper and the taxonomy
it provides should benefit practitioners and researchers alike in the
increasingly important area of private graph data release and analysis
A Survey on Actionable Knowledge
Actionable Knowledge Discovery (AKD) is a crucial aspect of data mining that
is gaining popularity and being applied in a wide range of domains. This is
because AKD can extract valuable insights and information, also known as
knowledge, from large datasets. The goal of this paper is to examine different
research studies that focus on various domains and have different objectives.
The paper will review and discuss the methods used in these studies in detail.
AKD is a process of identifying and extracting actionable insights from data,
which can be used to make informed decisions and improve business outcomes. It
is a powerful tool for uncovering patterns and trends in data that can be used
for various applications such as customer relationship management, marketing,
and fraud detection. The research studies reviewed in this paper will explore
different techniques and approaches for AKD in different domains, such as
healthcare, finance, and telecommunications. The paper will provide a thorough
analysis of the current state of AKD in the field and will review the main
methods used by various research studies. Additionally, the paper will evaluate
the advantages and disadvantages of each method and will discuss any novel or
new solutions presented in the field. Overall, this paper aims to provide a
comprehensive overview of the methods and techniques used in AKD and the impact
they have on different domains
Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks
The success of modern applications depends on the insights they collect from their data repositories. Data repositories for such applications currently exceed exabytes and are rapidly increasing in size, as they collect data from varied sources - web applications, mobile phones, sensors and other connected devices. Distributed storage and data-centric compute frameworks have been invented to store and analyze these large datasets. This dissertation focuses on extending the applicability and improving the efficiency of distributed data-centric compute frameworks
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