CFP:Computers & Security Journal Special Issue on Privacy in Computing with Big Data and AI

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The rapid growth of the ICT technologies makes today’s society highly digitized and connected, and the advent of even newer technologies such as Internet of Things (IoT) and Cyber-Physical Systems (CPS) further extends the hyper digitization and connectivity from the cyber space into the physical space. In this cyber-physical space, many of us reply on different online and physical services to live our lives and do businesses. This leads to more and more personal data being disclosed to organizations behind such services and other people using such services. Many organizations now have access to data from a large number of people, allowing them to do big data analytics and to offer more targeted (i.e., personalized) services to their customers such as behavioral advertising. This leads to the digital data economy era, and data is incrementally seen as the key to drive future innovations. While the big data landscape benefits many human users through better services, more and more privacy issues also arise such as more frequent and larger-scale data breach incidents. Although many privacy enhancement technologies (PETs) have offered solutions to protect our personal data, many still have practical limitations (e.g., not effective or efficient enough, less user-centric). The big data vs. privacy dilemma calls for more research on privacy computing as a special sub-area of cyber security, which requires involving researchers and practitioners from many other disciplines such as mathematics, electronic engineering, business, law, psychology, economics, sociology. In addition, research on big data based privacy computing also has a lot of overlaps with AI, e.g., on privacy attacks based on AI, privacy leakage from AI models, privacy and ethical issues related to AI, and new paradigms of AI models that are more privacy-aware or privacy-friendly. The aim of this special issue is to create a venue for privacy computing research from different disciplines to meet.

Topics of interest include but are not limited to the following:

  • Metrics for privacy computing such as differential privacy
  • Privacy of or by AI (machine learning, data mining and knowledge discovery) e.g. privacy-preserving learning and federated learning
  • Privacy operation and modelling
  • Privacy attacks e.g. AI-based attacks or privacy leakage from AI models
  • Computational taxonomies and ontologies for privacy computing
  • User-centric and personalized privacy computing in the big data context
  • Trade-off between privacy and security, trust, autonomy, reliability, resilience, fault-tolerance
  • Private information collection, storage, aggregation and retrieval
  • Privacy in different big-data contexts such as online social networks, healthcare, IoT and e-government
  • Privacy issues in blockchains and cryptocurrencies
  • Interaction between technologies and data protection / privacy law such as the EU GDPR
  • Interactions between privacy computing and psychology and wider social sciences such as privacy concerns, privacy paradox, privacy calculus, and privacy decision making
  • Interactions between privacy computing and business research such as privacy management and data protection impact assessment

Submission Guidelines:

To submit the paper, use the following link:

When submitting your manuscript please select the article type “VSI: PrivacyComptingBigDataAI“. Please submit your manuscript before the submission deadline (see below).

All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production.

Please ensure you read the following general Guide for Authors before writing your manuscript:

Important Dates:

  • Submission deadline: January 7, 2020
  • First Round of Reviews: February 29, 2020
  • Revised Manuscript: April 30, 2020
  • Final Decision: June 30, 2020

Guest Editors: