Authentication on the Internet usually has the drawback of leaking the identity of the users, or at least allowing to trace them from a server to another. Anonymous credentials overcome this issue, by allowing users to reveal the attributes necessary for the authentication, without revealing any other information (in particular not their identity). In this article, we provide a generic framework to construct anonymous credential schemes and use it to give a concrete construction of post-quantum (lattice-based) anonymous credential protocol. Our protocol thus allows for long-term security even when one considers the emergence of quantum computers able to break widely used traditional computational assumptions, such as RSA, the discrete logarithm or Diffie-Hellman. We also give a concrete implementation of our protocol, which is only one order of magnitude slower and bandwidth consuming than previous anonymous credentials that are not post-quantum.

Machine Learning is a well established tool used in a variety of applications. As training advanced models requires considerable amounts of meaningful data in addition to specific knowledge, a new business model separate models creators from model users. Pre-trained models are sold or made available as a service. This raises several security challenges, among others the one of intellectual property protection. Therefore, a new research track actively seeks to provide techniques for model watermarking that would enable model identification in case of suspicion of model theft or misuse. In this paper, we focus on the problem of secure watermarks verification, which affects all of the proposed techniques and until now was barely tackled. First, we revisit the existing threat model. In particular, we explain the possible threats related to a semi-honest or dishonest verification authority. Secondly, we show how to reduce trust requirements between participants by performing the watermarks verification on encrypted data. Finally, we describe a novel secure verification protocol as well as detail its possible implementation using Multi-Party Computation. The proposed solution does not only preserve the confidentiality of the watermarks but also helps detecting evasion attacks. It could be adopted to work with other authentication schemes based on watermarking, especially with image watermarking schemes.