12,636 research outputs found
User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy
Recommender systems have become an integral part of many social networks and
extract knowledge from a user's personal and sensitive data both explicitly,
with the user's knowledge, and implicitly. This trend has created major privacy
concerns as users are mostly unaware of what data and how much data is being
used and how securely it is used. In this context, several works have been done
to address privacy concerns for usage in online social network data and by
recommender systems. This paper surveys the main privacy concerns, measurements
and privacy-preserving techniques used in large-scale online social networks
and recommender systems. It is based on historical works on security,
privacy-preserving, statistical modeling, and datasets to provide an overview
of the technical difficulties and problems associated with privacy preserving
in online social networks.Comment: 26 pages, IET book chapter on big data recommender system
Privacy Preserving Utility Mining: A Survey
In big data era, the collected data usually contains rich information and
hidden knowledge. Utility-oriented pattern mining and analytics have shown a
powerful ability to explore these ubiquitous data, which may be collected from
various fields and applications, such as market basket analysis, retail,
click-stream analysis, medical analysis, and bioinformatics. However, analysis
of these data with sensitive private information raises privacy concerns. To
achieve better trade-off between utility maximizing and privacy preserving,
Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent
years. In this paper, we provide a comprehensive overview of PPUM. We first
present the background of utility mining, privacy-preserving data mining and
PPUM, then introduce the related preliminaries and problem formulation of PPUM,
as well as some key evaluation criteria for PPUM. In particular, we present and
discuss the current state-of-the-art PPUM algorithms, as well as their
advantages and deficiencies in detail. Finally, we highlight and discuss some
technical challenges and open directions for future research on PPUM.Comment: 2018 IEEE International Conference on Big Data, 10 page
The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges
The Internet of Things (IoT) refers to a network of connected devices
collecting and exchanging data over the Internet. These things can be
artificial or natural, and interact as autonomous agents forming a complex
system. In turn, Business Process Management (BPM) was established to analyze,
discover, design, implement, execute, monitor and evolve collaborative business
processes within and across organizations. While the IoT and BPM have been
regarded as separate topics in research and practice, we strongly believe that
the management of IoT applications will strongly benefit from BPM concepts,
methods and technologies on the one hand; on the other one, the IoT poses
challenges that will require enhancements and extensions of the current
state-of-the-art in the BPM field. In this paper, we question to what extent
these two paradigms can be combined and we discuss the emerging challenges
A European research roadmap for optimizing societal impact of big data on environment and energy efficiency
We present a roadmap to guide European research efforts towards a socially
responsible big data economy that maximizes the positive impact of big data in
environment and energy efficiency. The goal of the roadmap is to allow
stakeholders and the big data community to identify and meet big data
challenges, and to proceed with a shared understanding of the societal impact,
positive and negative externalities, and concrete problems worth investigating.
It builds upon a case study focused on the impact of big data practices in the
context of Earth Observation that reveals both positive and negative effects in
the areas of economy, society and ethics, legal frameworks and political
issues. The roadmap identifies European technical and non-technical priorities
in research and innovation to be addressed in the upcoming five years in order
to deliver societal impact, develop skills and contribute to standardization.Comment: 6 pages, 2 figures, 1 tabl
Personal information privacy: what's next?
In recent events, user-privacy has been a main focus for all technological and data-holding companies, due to the global interest in protecting personal information. Regulations like the General Data Protection Regulation (GDPR) set firm laws and penalties around the handling and misuse of user data. These
privacy rules apply regardless of the data structure, whether it being structured or unstructured. In this work, we perform a summary of the available algorithms for providing privacy in structured data, and analyze the popular tools that handle privacy in textual data; namely medical data. We found that although these tools provide adequate results in terms of de-identifying medical records by removing personal identifyers (HIPAA PHI), they fall short in terms of being generalizable to satisfy nonmedical fields. In addition, the metrics
used to measure the performance of these privacy algorithms don't take into account the differences in significance that every identifier has. Finally, we propose the concept of a domain-independent adaptable system that learns the significance of terms in a given text, in terms of person identifiability and text utility, and is then able to provide metrics to help find a balance between user privacy and data usability
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