Conference Information
ICDIS 2022: International Conference on Data Intelligence and Security
Submission Date:
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Conference Date:
Shenzhen, China
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Call For Papers
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
    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
Last updated by Dou Sun in 2021-11-30
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