Information Security in AI
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 1185
Special Issue Editors
Interests: symmetric/asymmetric ciphers; Blockchain; AI security
Interests: cryptographic protocols; provable security; electronic voting
Special Issues, Collections and Topics in MDPI journals
Interests: information security; AI security; cryptographic protocols
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, the application of artificial intelligence (AI) technologies has become increasingly widespread in fields such as autonomous driving, industrial IoT, healthcare, and natural language processing. However, information security issues have become one of the key factors constraining the large-scale application of AI systems. For example, model training requires large amounts of data, but users often hesitate to share their data to protect their privacy, leading to the problem of data silos. This necessitates the development of methods that enable model training without compromising user privacy and also ensuring the trained models are privacy-preserving. Furthermore, using AI methods to identify security vulnerabilities and help enhance the security of information systems is also becoming an effective approach in designing secure systems.
The aim of this Special Issue is to bring together research investigations in identifying information security issues in AI systems, building secure and robust AI protocols and systems, and developing AI-based security enhancement techniques. Authors are invited to submit original research contributions on topics including but not limited to the following:
- Data, model, and membership inference attacks;
- Data and model poisoning attacks;
- Adversarial training and certified robustness;
- Privacy and security in federated learning;
- Privacy-preserving computations;
- Symmetric and asymmetric cryptographic protocols;
- Key management through symmetric bivariate polynomials;
- Information security in AI applications;
- Security and privacy in AIGC;
- AI-enabled digital investigation;
- The integration of AI and Blockchain for security critical infrastructures.
Dr. Jiageng Chen
Dr. Zhe Xia
Dr. Chunhua Su
Guest Editors
Manuscript Submission Information
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Keywords
- data, model, and membership inference attacks
- data and model poisoning attacks
- adversarial training and certified robustness
- privacy and security in federated learning
- privacy-preserving computations
- symmetric and asymmetric cryptographic protocols
- key management through symmetric bivariate polynomials
- information security in AI applications
- security and privacy in AIGC
- AI-enabled digital investigation
- the integration of AI and Blockchain for security critical infrastructures.
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