Watermarking PRFs against Quantum Adversaries
Fuyuki Kitagawa, Ryo Nishimaki

TL;DR
This paper introduces the concept of watermarking pseudorandom functions (PRFs) resilient against quantum attacks, proposing constructions based on quantum hardness assumptions and developing quantum extraction techniques.
Contribution
It defines secure quantum watermarking PRFs and provides two constructions, one privately extractable and one publicly extractable, using quantum hardness assumptions and indistinguishability obfuscation.
Findings
Constructed privately extractable watermarking PRF from quantum LWE
Developed publicly extractable watermarking PRF using IO and quantum LWE
Introduced quantum extraction technique for information retrieval from quantum states
Abstract
We initiate the study of software watermarking against quantum adversaries. A quantum adversary generates a quantum state as a pirate software that potentially removes an embedded message from a classical marked software. Extracting an embedded message from quantum pirate software is difficult since measurement could irreversibly alter the quantum state. In this work, we define secure watermarking PRFs for quantum adversaries (unremovability against quantum adversaries). We also present two watermarking PRFs as follows. - We construct a privately extractable watermarking PRF against quantum adversaries from the quantum hardness of the learning with errors (LWE) problem. The marking and extraction algorithms use a public parameter and a private extraction key, respectively. The watermarking PRF is unremovable even if adversaries have (the public parameter and) access to the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPhysical Unclonable Functions (PUFs) and Hardware Security · Adversarial Robustness in Machine Learning · Cryptography and Data Security
