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Call For Papers (txt version)

Aim and Scope


Data intelligence and data security are two closely related areas.  In the era of big data, both data intelligence and data security are very important, and present constant challenges for both academia and industry. Those challenges bring with great opportunities for innovative ideas, tools and technologies.


The 4th International Conference on Data Intelligence and Security (ICDIS-2022) aims to:

(1) provide a unique forum where data intelligence and data security are all involved;

(2) provide a forum for researchers, experts, professionals and stakeholders in related fields to disseminate their recent advances and share their views on future perspectives.


The topics of ICDIS-2022 include two aspects. First, contributions on data intelligence in security and privacy are welcome, including works on how to learn from data and how to intelligently process data for security and privacy applications. Second, contributions on security and privacy in data intelligence are always within the scope of the conference, including works on making data intelligence models secure and trusted. 

Particularly, the topics of interest include but are not limited to:

Topic 1: Data Intelligence in Security and Privacy

  • Intrusion detection

  • Anomaly detection

  • Fraud detection

  • Defense against Malicious codes

  • Defense against denial of service attacks

  • Network security

  • System security

  • Biometrics

  • Deep learning

  • Unsupervised learning and clustering

  • Supervised learning and classification

  • Reinforcement learning

  • Data mining

  • Robust and dynamic optimization

  • Visualization and analysis

  • Immune computation

Topic 2: Security and Privacy in Data Intelligence

  • Federated learning

  • Swarm learning

  • Poisoning attack and defense

  • Evasion attacks and defense

  • Adversarial examples

  • Model inversion

  • AI backdoors

  • Membership inference attacks

  • Digital watermarking for AI models

  • Privacy-preserving machine learning

  • Privacy-preserving data mining

  • Privacy-preserving data publishing

  • Secure model processing platforms

  • Security and privacy in social networks

  • Interpretability of machine learning models for secure machine learning

  • Secure machine learning

  • Secure cloud computing

  • Secure multi-party computation

  • Data privacy

  • Sensitive data collection

  • AI fairness

  • AI trust

  • AI ethics

  • Blockchain

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