Sketching Meets Differential Privacy: Fast Algorithm for Dynamic Kronecker Projection Maintenance

Projection maintenance is one of the core data structure tasks. Efficient data structures for projection maintenance have led to recent breakthroughs in many convex programming algorithms. In this work, we further extend this framework to the Kronecker product structure. Given a constraint matrix sf A and a positive semi-definite matrix W∈ ℝn× n with a sparse eigenbasis, we consider the task of maintaining the projection in the form of sf B^top(sf Bsf B^top)-1sf B, where sf B=sf A(Wotimes I) or sf B=sf A(W1/2otimes W1/2). At each iteration, the weight matrix W receives a low rank change and we receive a new vector h. The goal is to maintain the projection matrix and answer the query sf B^top(sf Bsf B^top)-1sf Bh with good approximation guarantees. We design a fast dynamic data structure for this task and it is robust against an adaptive adversary. Following the beautiful and pioneering work of [Beimel, Kaplan, Mansour, Nissim, Saranurak and Stemmer, STOC'22], we use tools from differential privacy to reduce the randomness required by the data structure and further improve the running time.