About AICrypt
The intersection of artificial intelligence (AI) and security has gained significant attention, driven by the need for secure solutions that deploy AI. Cryptography, in particular, stands as a notable example of the benefits of AI integration. AI techniques and methods are already being applied to address challenges in cryptography, such as improving cryptanalysis and physical attacks and relevant countermeasures. Additionally, the use of cryptography to address security and privacy issues in AI systems is emerging as a crucial area of focus. As attacks on AI systems become more powerful, there is a growing need to explore how cryptographic strategies can mitigate these threats. Examples include the development of cryptographic backdoors in neural networks, the use of cryptographic techniques to watermark the output of LLMs, or model stealing attacks based on cryptanalysis techniques.
Our goal is to bring together experts from academia and industry, each with a unique perspective on cryptography and AI, to foster knowledge exchange and collaborative innovation. We are particularly interested in exploring how techniques can be transferred across different cryptographic applications and in enhancing AI security mechanisms. Moreover, we will review recent advancements, including those discussed at previous AICrypt events, to provide a comprehensive understanding of this rapidly evolving field.
Topics of Interest
Authors interested in giving a contributed talk in AICrypt are invited to submit an extended abstract of at most 2 pages (excluding references) on Easychair.
Topics of interest for this workshop include, but are not limited to:
- - Deep learning-based cryptanalysis (e.g., neural distinguishers)
- - Explainability and interpretability of AI models for cryptanalysis
- - Deep learning techniques for Side-Channel Analysis
- - AI-assisted design of cryptographic primitives and protocols
- - AI-driven attacks on cryptographic protocols (e.g., searchable symmetric encryption)
- - Application of homomorphic encryption and secure multiparty computation for privacy-preserving ML
- - Cryptographic approaches to enforce security and traceability of AI models (e.g., cryptographic backdoors of neural networks and statistical watermarking of LLM-generated content)
Submitted abstracts for contributed talks will be reviewed by the workshop chairs for suitability and interest to the AICrypt audience. There are no formal proceedings published in this workshop. Thus, authors can submit extended abstracts related to works submitted or recently published in other venues or work in progress that they plan to submit elsewhere.
