arXiv subjects

Muthuramakrishnan Venkitasubramaniam

Publications and source records attributed to Muthuramakrishnan Venkitasubramaniam.

3 recordsLinked to original sources

arXiv2026

Lower Bounds on Black-Box Constructions of Pseudorandom Functions

In their seminal work, Goldreich, Goldwasser, and Micali [CRYPTO 1984] constructed a pseudorandom function (PRF) using a black-box access to a pseudorandom generator (PRG). When combined with Levin's domain extension technique, the GGM construction invokes the PRG $ω(\log n)$ times, where $n$ denotes the input length to the PRG. To this day, no black-box construction achieving fewer calls is known. Recently, Beimel, Malkin, and Mazor [CRYPTO 2024] showed that for a certain family of constructions, which they termed \emph{tree constructions}, the GGM construction is optimal. However, the basic challenge of whether a PRF can be built with just \emph{one invocation} of the PRG still remains open. In this work, we consider fully black-box constructions of PRFs from PRGs, where both the construction and the reduction are required to be black-box, and the number of interactions the reduction makes with the adversary is independent of the number of oracle calls the adversary makes to its underlying function within each interaction. Our main result shows that no such construction can have $o(n/\log n)$ and $o(\mathsf{in}/\log\mathsf{in})$ \emph{non-adaptive} calls to the PRG, where $\mathsf{in}$ is the input length of the PRF. This impossibility holds even for weak PRFs with one-bit output, where the adversary is restricted to making i.i.d. uniformly random queries. In addition, we prove a lower bound for weak PRFs with sufficiently long outputs that holds even when the construction is allowed to make adaptive queries to the PRG.

cs.CR↗

arXiv2020

Is it Easier to Prove Theorems that are Guaranteed to be True?

Consider the following two fundamental open problems in complexity theory: (a) Does a hard-on-average language in NP imply the existence of one-way functions?, or (b) Does a hard-on-average language in NP imply a hard-on-average problem in TFNP (i.e., the class of total NP search problem)? Our main result is that the answer to (at least) one of these questions is yes. Both one-way functions and problems in TFNP can be interpreted as promise-true distributional NP search problems---namely, distributional search problems where the sampler only samples true statements. As a direct corollary of the above result, we thus get that the existence of a hard-on-average distributional NP search problem implies a hard-on-average promise-true distributional NP search problem. In other words, "It is no easier to find witnesses (a.k.a. proofs) for efficiently-sampled statements (theorems) that are guaranteed to be true." This result follows from a more general study of interactive puzzles---a generalization of average-case hardness in NP---and in particular, a novel round-collapse theorem for computationally-sound protocols, analogous to Babai-Moran's celebrated round-collapse theorem for information-theoretically sound protocols. As another consequence of this treatment, we show that the existence of O(1)-round public-coin non-trivial arguments (i.e., argument systems that are not proofs) imply the existence of a hard-on-average problem in NP/poly.

cs.CC↗

arXiv2018

Outsourcing Private Machine Learning via Lightweight Secure Arithmetic Computation

In several settings of practical interest, two parties seek to collaboratively perform inference on their private data using a public machine learning model. For instance, several hospitals might wish to share patient medical records for enhanced diagnostics and disease prediction, but may not be able to share data in the clear because of privacy concerns. In this work, we propose an actively secure protocol for outsourcing secure and private machine learning computations. Recent works on the problem have mainly focused on passively secure protocols, whose security holds against passive (`semi-honest') parties but may completely break down in the presence of active (`malicious') parties who can deviate from the protocol. Secure neural networks based classification algorithms can be seen as an instantiation of an arithmetic computation over integers. We showcase the efficiency of our protocol by applying it to real-world instances of arithmetized neural network computations, including a network trained to perform collaborative disease prediction.

cs.CR↗