A function f is extractable if it is possible to algorithmically ``extract,'' from any adversarial program that outputs a value y in the image of f, a preimage of y.
When combined with hardness properties such as one-wayness or collision-resistance, extractability has proven to be a powerful tool. However, so far, extractability has not been explicitly shown. Instead, it has only been considered as a non-standard *knowledge assumption* on certain functions.
We make two headways in the study of the existence of extractable one-way functions (EOWFs). On the negative side, we show that if there exist indistinguishability obfuscators for a certain class of circuits then
there do not exist EOWFs where extraction works for any adversarial program with auxiliary-input of unbounded polynomial length.
On the positive side, for adversarial programs with bounded auxiliary-input (and unbounded polynomial running time), we give the first construction of EOWFs with an explicit extraction procedure, based on relatively standard assumptions (e.g., sub-exponential hardness of Learning with Errors). We then use these functions to construct the first 2-message zero-knowledge arguments and 3-message zero-knowledge arguments of knowledge, against the same class of adversarial verifiers, from essentially the same assumptions.
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