Biometrics, as an authentication tool, provides several advantages over conventional what you know (e.g., password, PIN) and what you possess (e.g., keys, tokens) authentication methods. However, a biometrics is an irrevocable password as we can’t change the biometrics easily. If it is compromised digitally, it is compromised for ever. Secondly, a biometrics can be easily matched against multiple databases to link identities. In order to alleviate privacy deficiencies of biometrics, IBM Research has pioneered a new technique for protecting biometrics templates that can allow for revocation and anonymous sharing. Instead of enrolling with the true biometrics, the original signal/template is intentionally and repeatably distorted using a class of non-invertible functions. The resulting “transformed” biometrics is enrolled. During verification, the same distortion transformation is applied to the biometrics signal/template to match against the enrolled template. The proposed method supports revocability and permits anonymous matching where biometrics data sharing is prohibited.