We study the problem of constructing proof systems that achieve both soundness and zero knowledge unconditionally (without relying on intractability assumptions). Known techniques for this goal are primarily *combinatorial*, despite the fact that constructions of interactive proofs (IPs) and probabilistically checkable proofs (PCPs) heavily rely on *algebraic* techniques to achieve their properties.

We present simple and natural modifications of well-known "algebraic" IP and PCP protocols that achieve unconditional (perfect) zero knowledge in recently introduced models, overcoming limitations of known techniques.

1. We modify the PCP of Ben-Sasson and Sudan [BS08] to obtain zero knowledge for NEXP in the model of Interactive Oracle Proofs [BCS16,RRR16], where the verifier, in each round, receives a PCP from the prover.

2. We modify the IP of Lund, Fortnow, Karloff, and Nisan [LFKN92] to obtain zero knowledge for #P in the model of Interactive PCPs [KR08], where the verifier first receives a PCP from the prover and then interacts with him.

The simulators in our zero knowledge protocols rely on solving a problem that lies at the intersection of coding theory, linear algebra, and computational complexity, which we call the *succinct constraint detection* problem, and consists of detecting dual constraints with polynomial support size for codes of exponential block length. Our two results rely on solutions to this problem for fundamental classes of linear codes:

* An algorithm to detect constraints for Reed--Muller codes of exponential length. This algorithm exploits the Raz--Shpilka [RS05] deterministic polynomial identity testing algorithm, and shows, to our knowledge, a first connection of algebraic complexity theory with zero knowledge.

* An algorithm to detect constraints for PCPs of Proximity of Reed--Solomon codes [BS08] of exponential degree. This algorithm exploits the recursive structure of the PCPs of Proximity to show that small-support constraints are "locally" spanned by a small number of small-support constraints.

Revised full version.

We present the first constructions of *single*-prover proof systems that achieve *perfect* zero knowledge (PZK) for languages beyond NP, under no intractability assumptions:

1. The complexity class #P has PZK proofs in the model of Interactive PCPs (IPCPs) [KR08], where the verifier first receives from the prover a PCP and then engages with the prover in an Interactive Proof (IP).

2. The complexity class NEXP has PZK proofs in the model of Interactive Oracle Proofs (IOPs) [BCS16,RRR16], where the verifier, in every round of interaction, receives a PCP from the prover.

Unlike PZK multi-prover proof systems [BGKW88], PZK single-prover proof systems are elusive: PZK IPs are limited to AM ? coAM [F87,AH91], while known PCPs and IPCPs achieve only *statistical* simulation [KPT97,GIMS10]. Recent work [BCGV16] has achieved PZK for IOPs but only for languages in NP, while our results go beyond it.

Our constructions rely on *succinct* simulators that enable us to "simulate beyond NP", achieving exponential savings in efficiency over [BCGV16]. These simulators crucially rely on solving a problem that lies at the intersection of coding theory, linear algebra, and computational complexity, which we call the *succinct constraint detection* problem, and consists of detecting dual constraints with polynomial support size for codes of exponential block length. Our two results rely on solutions to this problem for fundamental classes of linear codes:

* An algorithm to detect constraints for Reed--Muller codes of exponential length.

* An algorithm to detect constraints for PCPs of Proximity of Reed--Solomon codes [BS08] of exponential degree.

The first algorithm exploits the Raz--Shpilka [RS05] deterministic polynomial identity testing algorithm, and shows, to our knowledge, a first connection of algebraic complexity theory with zero knowledge. Along the way, we give a perfect zero knowledge analogue of the celebrated sumcheck protocol [LFKN92], by leveraging both succinct constraint detection and low-degree testing. The second algorithm exploits the recursive structure of the PCPs of Proximity to show that small-support constraints are "locally" spanned by a small number of small-support constraints.