Big Data Privacy: Challenges to Privacy Principles and Models
TLDR
How well the two main privacy models used in anonymization meet the requirements of big data, namely composability, low computational cost and linkability is evaluated.Abstract:
This paper explores the challenges raised by big data in privacy-preserving data management. First, we examine the conflicts raised by big data with respect to preexisting concepts of private data management, such as consent, purpose limitation, transparency and individual rights of access, rectification and erasure. Anonymization appears as the best tool to mitigate such conflicts, and it is best implemented by adhering to a privacy model with precise privacy guarantees. For this reason, we evaluate how well the two main privacy models used in anonymization (k-anonymity and \(\varepsilon \)-differential privacy) meet the requirements of big data, namely composability, low computational cost and linkability.read more
Citations
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Journal ArticleDOI
Privacy-preserving cloud computing on sensitive data: A survey of methods, products and challenges
TL;DR: This survey covers technologies that allow privacy-aware outsourcing of storage and processing of sensitive data to public clouds and reviews masking methods for outsourced data based on data splitting and anonymization, in addition to cryptographic methods covered in other surveys.
Journal ArticleDOI
An Ethics Framework for Big Data in Health and Research
Vicki Xafis,G. Owen Schaefer,Markus K. Labude,Iain Brassington,Angela Ballantyne,Hannah Yeefen Lim,Wendy Lipworth,Tamra Lysaght,Cameron Stewart,Shirley Hsiao-Li Sun,Graeme Laurie,E. Shyong Tai +11 more
TL;DR: An Ethics Framework for Big Data in Health and Research developed by a working group convened by the Science, Health and Policy-relevant Ethics in Singapore (SHAPES) Initiative is presented, supported by the underlying ethical concerns that relate to all health and research contexts.
Posted ContentDOI
Privacy by design in big data: An overview of privacy enhancing technologies in the era of big data analytics.
Giuseppe D'Acquisto,Josep Domingo-Ferrer,Panayiotis Kikiras,Vicenç Torra,Yves-Alexandre de Montjoye,Athena Bourka +5 more
TL;DR: An analysis of the proposed privacy by design strategies in the different phases of the big data value chain, and a review of privacy enhancing technologies of special interest for the current and future big data landscape.
Book
Database Anonymization: Privacy Models, Data Utility, and Microaggregation-based Inter-model Connections
TL;DR: This Synthesis Lecture provides a comprehensive overview of the fundamentals of privacy in data releases focusing on the computer science perspective and identifies and discusses in detail connections between several privacy models and links between anonymization methods and privacy models.
Proceedings ArticleDOI
An Attribute-Based Access Control Model for Secure Big Data Processing in Hadoop Ecosystem
TL;DR: A fine-grained attribute-based access control model, referred as HeABAC, catering to the security and privacy needs of multi-tenant Hadoop ecosystem is presented, including the novel concept of cross Hadoops services trust.
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