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ICDIS 2022: International Conference on Data Intelligence and Security

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ICDIS
投稿締切日:
2022-03-15
通知日:
2022-05-01
開催日:
2022-07-25
開催地:
Shenzhen, China
開催回数:
4
閲覧: 18169   フォロー: 0   参加: 3

論文募集

ICDIS 2022 (International Conference on Data Intelligence and Security) is an academic conference held in Shenzhen, China on 2022-07-25. The paper submission deadline is 2022-03-15. Acceptance notifications are sent on 2022-05-01.

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. Themes 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
最終更新：Dou Sun（）

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関連ジャーナル

CCF正式名称インパクトファクター出版社ISSN
AIEEE Transactions on Multimedia9.7IEEE1520-9210
CKnowledge-Based Systems7.2Elsevier0950-7051
BSoftware & Systems Modeling3.2Springer1619-1366
AIEEE Transactions on Computers3.8IEEE0018-9340
CFuture Generation Computer Systems6.1Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.9Elsevier0167-8655
BPattern Recognition7.6Elsevier0031-3203
IEEE Access3.6IEEE2169-3536
AIEEE Transactions on Dependable and Secure Computing7.5IEEE1545-5971

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