A library for running Functional Encryption on tensors
Functional encryption (FE) is a generalization of public-key encryption in which possessing a secret key allows one to learn a function of what the ciphertext is encrypting. Functional encryption extends the notion of public key encryption where one uses a public key pk and a secret key sk to respectively encrypt and decrypt some data. More precisely, pk is still used to encrypt data, but for a given function f, sk can be used to derive a functional decryption key dkf
which will be shared to users so that, given a ciphertext of x, they can decrypt f(x) but not x. In
particular, someone having access to dkf cannot learn anything about x other than f(x). Note also
that functions cannot be composed, since the decryption happens within the function evaluation.
Hence, only single quadratic functions can be currently securely evaluated.
Perfect correctness: Perfect correctness is achieved in functional encryption: ∀x ∈ X , f ∈ F, Pr[Dec(dkf , ct) = f(x)] = 1, where dkf ← KeyGen(msk, f) and ct ← Enc(pk, x). Note that this property is a very strict condition, which is not satisfied by exisiting fully homomorphic encryption schemes (FHE),
It will mask the private data and allow to evaluate a pre-trained model thanks to its decryption key dkf
pip install PyFE
[] to do