Similarity preserving is a key ingredient of cancellable biometric scheme design. The notion ensures the accuracy performance of the biometric systems can be preserved after the cancellable biometric technique is applied. Random Projection is among the most commonly adopted method in cancellable biometric schemes. However, it is reversible subject to certain conditions, which disrupts the template irreversibility criterion. This invites vulnerabilities for random projection-based schemes. In this paper, we propose a novel transform function, namely Absolute Value Equations Transform (AVET), which non-linearly projects feature vectors to another domain. The transformed templates hold two main merits ensuring the user’s privacy, and maintaining the system’s performance simultaneously. First, by relying on the hardness of the Absolute Value Equations problem, we guarantee that AVET satisfies irreversibility. Second, by using Johnson–Lindenstrauss lemma and the inverse triangle inequality, we prove that the proposed approach has the similarity preserving property. Notably, rigorous theoretical proofs and empirical experiments are provided. The efficacy of AVET is comprehensively evaluated on both physiological and behavioral biometrics including face, ear, fingerprint, and gait. With unimodal approach, we achieve competitive performances compared to related algorithms on eight public datasets. Regarding bimodal mode, the AVET surpasses the state-of-the-art technique on all three observed datasets. To the best of our knowledge, this is the first study that attempts to develop a secure transformation to augment the role of Random Projection in the existing cancellable biometric schemes