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The International Centre for Mathematical Sciences (ICMS)

While lattice-based cryptography has been successful, a consensus is forming that we have reached a limit of what we can do from such well-established problems. Thus, researchers are introducing novel variants of these presumed-hard problems to develop quantum-safe privacy-enhancing technologies (PETs). Many PETs lack efficient lattice-based or other efficient post-quantum constructions. Examples include advanced encryption schemes with fine-grained access control, blind signatures, anonymous credentials, oblivious PRFs, and threshold constructions. Adopting novel hardness assumptions reflects a long-standing tension in cryptography: more structure enables advanced functionalities but may offer flexibility to solve presumed-hard problems, invalidating their hardness. As these assumptions proliferate, the risk grows that they remain understudied and may lack the robustness to serve as foundations for a privacy-respecting post-quantum digital society.

Lancaster University, School of Computing and Communications

We are soliciting research papers on topics covering all well-motivated computer security problems. We care about techniques that identify new real-world threats, techniques to prevent them, to detect them, to mitigate them or to assess their prevalence and their consequences. Measurement papers are encouraged, as well as papers offering public access to new tools or datasets, or experience papers that clearly articulate important lessons learned.

Program Committee Chairs;     Email: pcchairs@raid2026.org

Cloud security; Cybercrime and underground economies; Cyber-physical systems security and threats against critical infrastructures; Denial-of-Service attacks and defenses; Digital forensics; Hardware security; Intrusion detection and prevention; IoT security; Machine learning for security; Malware and unwanted software; Mobile security and privacy; Network security; Program analysis and reverse engineering; Security education and training; Security measurement studies; Security of machine learning systems; Software security; Systems security; Statistical and adversarial learning for computer security; Usable security and privacy; Vulnerability analysis and exploitation techniques; Web security and privacy