Special Issue on Privacy in Computing with Big Data and AI
摘要截稿:
全文截稿: 2019-12-31
影响因子: 3.579
期刊难度:
CCF分类: B类
中科院JCR分区:
• 大类 : 计算机科学 - 2区
• 小类 : 计算机:信息系统 - 3区
Overview
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