How Robust are Linear Sketches to Adaptive Inputs?

TL;DR

Linear sketches are fundamentally non-robust to adaptively chosen inputs, failing to reliably approximate Euclidean norms under such conditions, which impacts their application in data streams, compressed sensing, and distributed computing.

Contribution

This paper proves that no linear sketch can reliably approximate Euclidean norms on adaptively chosen inputs, even with high-dimensional, unbounded computation, resolving open problems in compressed sensing.

Findings

Linear sketches fail on polynomially many adaptive inputs.

The failure persists even with high-dimensional, unbounded computation.

Implications for lp-norm estimation and compressed sensing are significant.

Abstract

Linear sketches are powerful algorithmic tools that turn an n-dimensional input into a concise lower-dimensional representation via a linear transformation. Such sketches have seen a wide range of applications including norm estimation over data streams, compressed sensing, and distributed computing. In almost any realistic setting, however, a linear sketch faces the possibility that its inputs are correlated with previous evaluations of the sketch. Known techniques no longer guarantee the correctness of the output in the presence of such correlations. We therefore ask: Are linear sketches inherently non-robust to adaptively chosen inputs? We give a strong affirmative answer to this question. Specifically, we show that no linear sketch approximates the Euclidean norm of its input to within an arbitrary multiplicative approximation factor on a polynomial number of adaptively chosen inputs. The result remains true even if the dimension of the sketch is d = n - o(n) and the sketch is given unbounded computation time. Our result is based on an algorithm with running time polynomial in d that adaptively finds a distribution over inputs on which the sketch is incorrect with constant probability. Our result implies several corollaries for related problems including lp-norm estimation and compressed sensing. Notably, we resolve an open problem in compressed sensing regarding the feasibility of l2/l2-recovery guarantees in the presence of computationally bounded adversaries.