*16:17*[Pub][ePrint] Faster Bootstrapping with Polynomial Error, by Jacob Alperin-Sheriff and Chris Peikert

\\emph{Bootstrapping} is a technique, originally due to Gentry (STOC

2009), for ``refreshing\'\' ciphertexts of a somewhat homomorphic

encryption scheme so that they can support further homomorphic

operations. To date, bootstrapping remains the only known way of

obtaining fully homomorphic encryption for arbitrary unbounded

computations.

Over the past few years, several works have dramatically improved the

efficiency of bootstrapping and the hardness assumptions needed to

implement it. Recently, Brakerski and Vaikuntanathan~(ITCS~2014)

reached the major milestone of a bootstrapping algorithm based on

Learning With Errors for \\emph{polynomial} approximation factors.

Their method uses the Gentry-Sahai-Waters~(GSW)

cryptosystem~(CRYPTO~2013) in conjunction with Barrington\'s ``circuit

sequentialization\'\' theorem~(STOC~1986). This approach, however,

results in \\emph{very large} polynomial runtimes and approximation

factors. (The approximation factors can be improved, but at even

greater costs in runtime and space.)

In this work we give a new bootstrapping algorithm whose runtime and

associated approximation factor are both \\emph{small} polynomials.

Unlike most previous methods, ours implements an elementary and

efficient \\emph{arithmetic} procedure, thereby avoiding the

inefficiencies inherent to the use of boolean circuits and

Barrington\'s Theorem. For $2^{\\lambda}$ security under conventional

lattice assumptions, our method requires only a \\emph{quasi-linear}

$\\Otil(\\lambda)$ number of homomorphic operations on GSW ciphertexts,

which is optimal (up to polylogarithmic factors) for schemes that

encrypt just one bit per ciphertext. As a contribution of independent

interest, we also give a technically simpler variant of the GSW system

and a tighter error analysis for its homomorphic operations.