Laconic Function Evaluation, Functional Encryption and Obfuscation for RAMs with Sublinear Computation

Laconic function evaluation (LFE) is a "flipped" version of fully homomorphic encryption, where the server performing the computation gets the output. The server commits itself to a function $f$ by outputting a small digest. Clients can later efficiently encrypt inputs $x$ with respect to the digest in much less time than computing $f$, and ensure that the server only decrypts $f(x)$, but does not learn anything else about $x$. Prior works constructed LFE for circuits under LWE, and for Turing Machines (TMs) from indistinguishability obfuscation (iO). In this work we introduce LFE for Random-Access Machines (RAM-LFE). The server commits itself to a potentially huge database $y$ via a short digest. Clients can later efficiently encrypt inputs $x$ with respect to the digest and the server decrypts $f(x,y)$ for some specified RAM program $f$ (e.g., a universal RAM), without learning anything else about $x$. The main advantage of RAM-LFE is that the server's decryption run-time only scales with the RAM run-time $T$ of the computation $f(x,y)$, which can be sublinear in both $|x|$ and $|y|$. We consider a weakly efficient variant, where the client's run-time is also allowed to scale linearly with $T$, but not $|y|$, and a fully efficient variant, where the client's run-time must be sublinear in both $T$ and $|y|$. We construct the former from doubly efficient private information retrieval (DEPIR) and laconic OT (LOT), both of which are known from RingLWE, and the latter from an additional use of iO. We then show how to leverage fully efficient RAM-LFE to also get (many-key) functional encryption for RAMs (RAM-FE) where secret keys are associate with big databases $y$ and the decryption time is sublinear in $|y|$, as well as iO for RAMs where the obfuscated program contains a big database $y$ and the evaluation time is sublinear in $|y|$.