Abstract. Secure sketches are useful in extending cryptographic schemes to biometric data since they allow recovery of fuzzy secrets under inevitable noise. In practice, secrets derived from biometric data are seldom used alone, but typically employed in a multi-factor or a multimodality setting

Given the original set X where |X| = s, a sketch P is computed from X and made public. From another set Y where |Y | = s and P , we can reconstruct X if |X # Y | # |s - t|, where t is some threshold. The sketch P is secure if it does not reveal much information about X. A few constructions have

Abstract. There have been active discussions on how to derive a consistent cryptographic key from noisy data such as biometric templates, with the help of some extra information called a sketch. It is desirable that the sketch reveals little information about the biometric templates even

We consider secure sketch construction in an asymmetric setting, that is, multiple samples are acquired during enrollment, but only a single sample is obtained during verification. Known protection methods apply secure sketch constructions on the average of the samples, while publishing

Abstract—Secure sketches and fuzzy extractors enable the use of biometric data in cryptographic applications by correcting errors in noisy biometric readings and producing cryptographic materials suitable for authentication, encryption, and other purposes. Such constructions work by producing a

Abstract. A noisy non-uniformly distributed secret often needs to be transformed into a stable high-entropy key. Biometric systems and physi-cally unclonable functions (PUFs) exemplify the need for this conversion. Secure sketches are a useful tool hereby as they alleviate the noisiness while

A secure sketch is a set of published data that can help to recover the original biometric data after they are corrupted by permissible noises, and by itself does not reveal much information about the original. Several constructions have been proposed for different metrics, and in particular, set

Set reconciliation is the problem of efficiently computing the difference be-tween two sets of data held by two different parties. We implement set recon-ciliation using the BCH-based secure sketch for the set-difference metric, as defined in [1], to solve this problem. This method publishes a

In order to apply cryptographic operations on noisy data, a recent approach employs some additional public data, known as secure sketch, to correct the noise so that consistent outcome can be obtained. This approach can be employed to extract authentication tags from noisy multimedia or biometric

if the input changes, as long as it remains reasonably close to the original. Thus, R can be used as a key in a cryptographic application. A secure sketch produces public information about its input w that does not reveal w, and yet allows exact recovery of w given another value that is close to w. Thus